kondeor.ai – Manual

Registration & Plans

You can try kondeor.ai for free without a credit card (Free plan) and upgrade to Basic or Pro at any time. During registration you agree to the Terms and Conditions and Privacy Policy — this applies even to the free plan, since it is a usage agreement as well.

For prices and limits, see Part E · Plan comparison.

If kondeor.ai is embedded in another page (e.g. a course platform), a fullscreen icon (⛶) appears in the top right — it opens kondeor.ai as a standalone page in a new browser tab, outside the embedding frame.

Uploading data

Two ways to get data into kondeor.ai:

• Upload a file — CSV or SPSS (.sav), via the upload button at the bottom left. SPSS files automatically bring variable labels, scale of measurement, and value labels; for CSV, kondeor.ai infers the scale of measurement heuristically from the values.
• Enter data directly — build a small table by hand using the table button, no file needed.

Without your own dataset, you always have access to the practice dataset GBOO (Austrian Guest & Visitor Survey, n = 1,153) — ideal for trying out methods before working with real data.

Data quality check

Right after every upload, kondeor.ai asks whether the dataset should be checked before analysis — with three options:

• no — skip the check, start analysing right away
• yes — basic check: missing values, outliers, constant columns, negative values, case-inconsistent categories, leading/trailing whitespace, and duplicate rows. Affected variables are grouped by issue type (🔴 critical, 🟡 warning).
• strict (Pro plan and above) — runs a deeper pattern check on top of the basic one: kondeor.ai automatically detects related item groups (scales) from the variable names and checks each case for suspicious response patterns — straightlining (the same answer throughout), extreme responding (only scale endpoints), and sawtooth patterns (regular zigzagging). If a timestamp variable is detected in the dataset, response duration is factored in too (median, 10th percentile).

The deep check's result shows a summary by issue type, plus how many cases are affected within each checked variable group. The complete, sortable list with row/ID is available in the results dashboard, including download as a standalone HTML report.

kondeor.ai only reports the issues — cleaning up (e.g. excluding or correcting suspicious cases) is up to you, for example via the subsample filter (see B3) or directly in the dataset editor (see A4).

Variable colour coding

Before running a calculation, kondeor.ai shows a confirmation card with the detected variables — the colour of each variable name shows the detected scale of measurement:

Dependent & independent variable

For methods with a target variable (e.g. regression, t-test), the card additionally shows which variable kondeor.ai has recognised as dependent (DV) and which as independent (IV) — as their own groups above the remaining variables. This is a display for review only, not a clickable selector: if the assignment doesn't match what you meant, correct it in the chat instead (see B1) rather than clicking around on the card itself.

"Why this method?"

Below the variable list, an expandable section explains in one sentence why this particular method fits the recognised combination of variables — e.g. "Since 'gender' has exactly two categories, a logistic regression fits." The section is expanded by default and can be collapsed if you don't need it. Meant as much for learning along the way as for double-checking.

If kondeor.ai detects two binary variables for which both a t-test (treating one variable as quasi-metric) and a chi-square test (both stay categorical) are methodologically defensible, this is made transparent right here — including the other option as an alternative, instead of silently proposing only one of the two.

Note on many predictors

If a regression method has noticeably more predictors than can be meaningfully interpreted given the available sample size (rule of thumb: at least around 10 cases per predictor), the card shows an additional note recommending you narrow the selection — e.g. by saying "only with the most important variables" (see B1).

Editing your dataset

Besides data entry, the table button also opens the "Edit dataset" tab — this lets you adjust the currently loaded dataset directly in the browser:

• Click any cell to edit it
• Add rows (+ Row) or delete them via the 🗑 icon
• Add variables/columns (+ Column) or delete them — caution: deleting a variable cannot be undone
• Paste values from Excel directly into a cell with Ctrl+V
• Sort by a column (display only — the saved row order stays unchanged)
• Variable view: adjust variable name, label, scale of measurement, and value labels

Saving datasets permanently

After every upload, kondeor.ai offers to keep the dataset permanently in your account — independent of the 60-minute session limit, for reuse in later sessions. Click "Save dataset" in the prompt that appears; "No thanks" dismisses the offer without affecting the current session.

The "Datasets" button always takes you to your saved datasets:

• Load — brings the dataset back into the current session, as if it had just been freshly uploaded
• Edit — opens it directly in the dataset editor (see A4)
• Context — add or change the questionnaire context (see below)
• Rename and Delete

Number of datasets you can keep at once: 3 on the Basis plan, 10 on the Pro plan. If the limit is reached, "Save dataset" reports this — delete an existing dataset first to make room for a new one.

Asking your first question

Just phrase your analysis question in plain language. For unambiguous matching, write variable names in single quotes:

• What is the median of 'age'?Descriptive statistics for a single variable
• Does 'income' differ by 'gender'?kondeor.ai automatically suggests the appropriate test (e.g. t-test or Mann-Whitney)
• Show me a complete overview of all the data.Automatic descriptive statistics across the entire dataset
• What hypothesis could I test for 'income' and 'gender'?Instead of running the analysis right away, kondeor.ai first formulates H0/H1 and suggests the appropriate method — useful when you're not yet sure which test you actually need

R syntax

If you're used to the classic R formula notation (dependent ~ independent), kondeor.ai understands that too — handy if you already think about regressions this way:

• 'grade' ~ 'gender' + 'age' + 'attendance'Linear or logistic regression, depending on the type of 'grade' — 'gender', 'age' and 'attendance' as predictors
• 'satisfaction' ~ 'gender'Also works with just one independent variable
• 'income' ~ 'education', 'age'A comma between predictors works just as well as a plus sign

If a question already names the method, dependent variable, and all independent variables fully and unambiguously in quotes (as in the examples above), kondeor.ai skips the usual confirmation card and runs the calculation right away — the recognised configuration then appears as a short, expandable summary next to the result instead of before it. If even one variable stays unclear or unquoted, the confirmation card appears as usual.

Theoretical background

Under every analysis result you'll find a "Show theoretical background" button — kondeor.ai then explains the statistical theory relevant to the specific question you asked, not just the method in general. Ask specifically about the degrees of freedom of a crosstab, for example, and you get an explanation of degrees of freedom, not just a generic chi-square introduction.

You can also ask about theory independently of a specific analysis — just ask a question about a statistical concept, without variable names in quotes:

• What's the difference between a confidence interval and a credible interval?
• When do I use Mann-Whitney instead of a t-test?

Answers are drawn from a curated knowledge base (several statistics textbooks), including a source citation at the end of every answer.

Help mode

For questions about using kondeor.ai itself — not about statistics, not about your data — there's a dedicated mode button "Help" next to Analysis/Theory/Power. In help mode, kondeor.ai treats your messages preferentially as usage questions, even if they don't contain a clear keyword — especially useful for follow-up questions like "and how exactly do I do that?", which would be hard to classify on their own.

• How do I compare two groups?Explains the matching question phrasing, doesn't run a calculation
• How do I filter my data down to a subset?
• How do I save a result to a report?
• Can you analyse the answers to open-ended questions?
• How do I group values together?Help mode also covers transformations — not just app usage itself, see B4

kondeor.ai also recognises most usage questions automatically outside help mode — the mode mainly helps with ambiguous follow-up questions in an ongoing conversation, where the connection to the previous message might otherwise get lost.

Exception: if your question in help mode names something that actually matches a column in your currently loaded dataset (e.g. "How do I compute X per Y?", where X and Y are real column names), kondeor.ai recognises this as a concrete transformation request and shows a formula preview right away instead of an explanation — usually the more useful outcome anyway.

Methods at a glance

kondeor.ai covers over 80 statistical methods, organised into the following analysis families:

Full, continuously maintained list of all methods incl. technical details: kondeor.at/de/portfolio/statistik-ki/methoden (opens directly in English via the page's own ?lang=en parameter — page chrome around the widget stays German, but the methods list itself loads in English).

Scale recognition & comparable studies (demo)

After upload, kondeor.ai automatically checks whether variable names match known, standardised questionnaire instruments (e.g. based on an abbreviation contained in the name, such as IPSS or FSFI). A match appears as its own chat message right after upload, with the recognised variables and a source reference for the instrument.

For some analysis methods, kondeor.ai additionally adds a brief note on which other studies known from the literature used the same statistical methodology — as context for orientation, not as an evaluation of your own result.

Both hints are deliberately labelled "🔬 Demo": the underlying knowledge base is continuously expanded with further fields and instruments, but doesn't yet cover every topic.

Follow-up questions & changing topics

After an analysis, you can directly dig deeper into the results — just keep going, kondeor.ai automatically refers back to the last analysis:

• What hypotheses can be derived from these results?
• Which group deviates the most?

To switch to a new topic (same data), simply ask the new question directly — if there's a misunderstanding, an explicit hint helps:

• I mean a new question – please ignore the last analysis.

Correcting the last analysis

If the last suggested or already-run analysis isn't quite right, you don't need to rephrase the whole question — a short, targeted correction in the chat is enough. This works both for a still-open confirmation card and for an already-finished result:

• Change 'age' as dependent to 'gender'.Swaps the target variable — the previous DV automatically becomes available as a possible independent variable. If the previous method no longer fits the new variable type, kondeor.ai switches automatically (see "Why this method?", A3)
• Without 'age'.Removes one or more variables from the analysis, the rest stays as is
• Also with 'gender'.Adds an extra variable without removing the existing ones — can be combined with "without" too
• Only with 'age' and 'gender'.Replaces the previous selection entirely with a new, closed list
• 'income' instead of 'age'.Replaces one specific variable with another

If a correction doesn't clearly relate to a question you've already asked (e.g. if you asked about something else entirely in between), kondeor.ai generally recognises that on its own and treats your message as a new, standalone question instead.

Analysis plan & batch mode

For structured work (e.g. a prepared survey analysis) you can save multiple analysis questions as an analysis plan and run them all at once in batch mode — both available from the Pro plan.

Generating an analysis plan automatically

Via the "Analysis Plan" toolbar button, describe your research question in your own words — kondeor.ai proposes a complete plan from it, each individual question already in the correct kondeor.ai format:

• Context questions (only if needed): Before the plan is created, kondeor.ai may ask one to three questions that concern the plan as a whole — e.g. whether the study is more exploratory or hypothesis-driven. Not every research question needs this; often the plan is generated right away.
• The plan itself: Each proposed analysis appears as its own card (title, question phrasing, H0/H1, a short justification for why this particular method fits the research question, and a confidence estimate). If kondeor.ai is unsure about one specific analysis — e.g. which of two plausible variables is meant —, a clickable choice appears directly on that card; the analysis is only finalised after your decision, the rest of the plan is unaffected.

Use "⚡ Load into batch" to move the finished plan directly into batch mode, or download it as a file via "⬇ Download .md".

Batch mode

The batch window holds one analysis question per line — either taken from the analysis plan or typed manually:

• Compare 'age' between 'gender' = 0 and 'gender' = 1
  Compare 'satisfaction' between 'gender' = 0 and 'gender' = 1
  Correlation between 'age' and 'satisfaction'Several questions in the batch field, one per line — kondeor.ai works through them one by one

kondeor.ai collects the individual results and, on request, generates an AI-assisted overall interpretation across all analyses — with four fixed sections (key findings, non-significant results, overall picture, methodological notes) plus a separate hypothesis summary: for every analysis with H0/H1, it states whether the hypothesis is confirmed, not confirmed, or ambiguous — including catching the case "significant, but in the wrong direction", which a plain p-value check would otherwise wave through as "confirmed".

After the batch run, "🔬 Suggest follow-up analyses" becomes available: kondeor.ai looks at the actual results (not just the original research question) and proposes sensible follow-up analyses from them — in the same card format as the original plan (title, question phrasing, H0/H1, justification, confidence), including a clickable choice where it's unsure. The suggestions arrive as a normal chat message, not automatically loaded into the batch field — you decide which ones to take.

Subset filters

You can restrict an analysis to a subset of your data without changing the dataset itself — the filter is set per session and can be viewed or removed at any time. Supported:

• Restrict the analysis to 'age' > 40 AND 'gender' = 1Multiple conditions, combined with AND
• Restrict the analysis to 'holiday_type' = 1 OR 'holiday_type' = 3Multiple conditions, combined with OR
• Restrict the analysis to cases without a missing value in 'satisfaction'Missing/not missing as its own condition, no value needed at all
• Restrict the analysis to cases where 'income' is greater than 'expenses'Cross-column comparison instead of a fixed value

In the data view, filtered-out rows are subtly highlighted in red instead of simply disappearing — so you always keep track of exactly what the filter excludes.

Transforming variables

Compute new variables, build categories, or recode values — either via the transform dialog or directly in plain language:

• Log-transform 'income' and build 3 groups from it.Multiple chained operations in a single request are supported
• Rank 'test_score', separately within each 'class'.Rank cases — smallest value = rank 1, ties get the average rank

kondeor.ai always shows the result as a preview first — nothing is applied automatically. Only clicking "Apply" actually writes the new variable into your working dataset.

Saving transformations as a recipe: Especially with more complex formulas or several chained steps, it's worth not re-typing them every time. The transform dialog offers "💾 Save as recipe" for this — a recipe is simply the currently assembled operation (or chain of operations) under a name you choose. Saved recipes can be loaded again anytime from the dropdown in the same dialog, edited before running, and either updated under the same name or saved as a new recipe. Recipes are deliberately stored across projects — like a personal formula library that can also be applied to other datasets. If a loaded recipe references a variable that doesn't exist in the current dataset (e.g. because it's named differently), this is flagged directly in the card instead of being silently dropped.

Four further operations are available in the transform dialog:

• Count occurrences — e.g. "how many of 5 items were answered yes": for each row, counts how many freely definable conditions (one per variable) are met
• Replace missing values — mean, median, or mode, optionally grouped (e.g. mean income per region); the original variable with its missing values is left unchanged, and an optional second column shows which cells were replaced. For more than a handful of missing cells, Multiple Imputation is preferable — see B9
• Rank cases — smallest value = rank 1, ties (equal values) get the average rank, optionally computed separately within groups
• Restructure Wide ↔ Long — for repeated-measures data: turns several time-point columns (e.g. anxiety_t1, anxiety_t2, anxiety_t3) into one row per person and time point, or vice versa. Needed, among other things, to prepare data for a multilevel (mixed models) analysis — which expects one row per observation.

Sample size calculator (power analysis)

Below the chat window you'll find an online calculator for the required sample size — usable immediately, no AI request needed. Two separate tabs, depending on your question:

• Power-based — for group comparisons and association tests: how many cases do I need to detect an assumed effect with a given statistical power? Covers t-test, ANOVA, correlation, chi-square, proportions, regression, and mediation.
• Precision-based — for classic upfront planning of a survey, with no hypothesis test involved: how many cases do I need to estimate a proportion or mean within a given margin of error? Optionally with a correction for small or known populations.

For more complex designs (e.g. multi-factor ANOVA, sensitivity analyses), we recommend G*Power.

APA check

For editors and anyone who wants to double-check published statistics: paste a reported statistic directly into the analysis question field — kondeor.ai recognises the pattern automatically and checks internal consistency.

• t(38) = -0.71, p = .481kondeor.ai recomputes the p-value from the statistic and degrees of freedom and reports whether it's consistent, a rounding discrepancy, or a decision error

Supported: t-test, F (ANOVA), Chi², correlation r, z, Mann-Whitney U, Kruskal-Wallis H, Wilcoxon. By default a pure internal consistency check of the reported values against each other, with no dataset needed. If a dataset is also loaded, you can have individual statistics recomputed directly against the actual data.

Writing results back into the dataset

For several methods (factor analysis, cluster analysis, regression, discriminant analysis, latent class analysis) you can write the computed values — e.g. factor scores, cluster membership, or residuals — into the working dataset as new column(s) with one click. For factor analysis/cluster, this happens using the parameters currently set in the dashboard (e.g. after changing the rotation or number of clusters); for the other methods, directly from the analysis result.

Afterwards, download the extended dataset via the toolbar button "Export dataset" (always as a .sav file, regardless of the original format).

👍/👎 on the method choice

The results dashboard shows two small thumbs icons next to the suggested method. Clicking one gives anonymous feedback on whether the automatically suggested method fit your question — this helps us keep improving automatic method selection. You can change your feedback at any time by simply clicking the other thumb instead.

Before rating, it's worth checking "Why this method?" right on the confirmation card (see A3) — it already explains why this particular method was suggested, often helpful for putting your own assessment in context.

See Privacy Policy, point 3.4, for details on how this feedback is processed anonymously.

Merging files

Combine two datasets into one — e.g. append a second wave of data collection, or bring in additional variables via a shared ID. The toolbar button "🔗" opens a dedicated dialog:

• Add cases — the second file is appended as additional rows (e.g. a further data-collection wave with the same variables).
• Add variables — the second file is joined to the current dataset via an ID column on both sides (e.g. additional follow-up variables for cases you already have).

kondeor.ai automatically matches column names between both files — even with different spelling or language (e.g. "geschlecht" and "gender" are recognised as the same). Nothing is decided automatically when uncertain: a mapping table shows every suggestion with a dropdown to review and override before anything is written.

For "add variables", the ID column is matched tolerantly as well: spellings like P07, p7, and 007 are recognised as the same person (case, leading zeros, and special characters don't matter). Cases that only appear in the second file are added as a new row instead of being discarded — the resulting missing columns from your original dataset stay blank.

Structural Equation Models, Multiple Imputation & Growth Curves

Three advanced extensions that all work on the same principle: kondeor.ai proposes a configuration based on your question, shows it in a confirmation card, and only runs the calculation after you click "Apply"/"Run" — nothing happens automatically in the background.

Confirmatory Factor Analysis (CFA) & Structural Equation Models (SEM)

• Confirmatory factor analysis with items item1, item2, item3 for the factor Satisfaction.
• Structural equation model: factor Satisfaction from item1-item3, factor Loyalty from item4-item5, Satisfaction affects Loyalty.Combined measurement and structural models are supported

The confirmation card shows the proposed factor/path structure in plain language and the raw lavaan syntax in an editable text field — you can correct the syntax directly or add your own covariances/second-order factors instead of relying on the automatic assignment. A checkbox lets you choose between listwise (complete cases only, default) and FIML (Full Information Maximum Likelihood — uses all available values, no row is dropped; methodologically superior to listwise deletion when data are missing at random).

Multiple Imputation

Unlike the simple "replace missing values" option in the transform dialog (see B4 — a single replacement value per missing cell), Multiple Imputation generates five complete, plausibly imputed versions of the dataset, runs the analysis on each one separately, and combines the five results using Rubin's rules into a single result that correctly accounts for imputation uncertainty — the methodological standard for missing data whenever more than a handful of cells are affected.

Available for t-test, Pearson correlation, and linear regression: a "Re-run with Multiple Imputation" button appears on the dashboard after any of these three analyses — the existing variables are picked up automatically, no need to re-describe the analysis. If the analysis has no missing values at all, kondeor.ai reports this briefly instead of running a pointless calculation.

Growth Curve Models

• Growth curve model for 'mood' over time, grouped by 'person_id'.
• Wachstumskurvenmodell für stimmung über die Zeit, gruppiert nach person_id.
• Quadratic growth curve model for 'score' over time, grouped by 'person_id', controlling for 'stress' over time.Curved trend + time-varying covariate in one request

Models how a measurement develops across repeated occasions — in two ways at once: as a multilevel model (random intercept + random slope per person) and as a latent growth model (structural equation model with intercept and slope factors). The confirmation card shows dropdowns for person ID, time, value, method, and growth form, plus a multi-select for covariates — all populated from your column names and editable before running. The dataset must be in long format (one row per person and occasion) — if it's wide (e.g. mood_t1, mood_t2, mood_t3), reshape it first using "Restructure wide ↔ long" (see B4).

The result shows the estimates from both models (if both were requested) as well as a trajectory chart: thin individual trajectories in the background plus a bold mean curve with a 95% confidence band — the core of the feature, not an afterthought.

Growth form: linear (default) or quadratic (an additional curvature term for a trend that flattens out or accelerates). For the multilevel model, curvature is estimated as a population-average effect (no individual differences in curvature per person — that would be a third random-effects level and would rarely converge reliably at typical sample sizes). For the latent model, a third factor is added instead.

Time-varying covariates: up to 5 additional variables that were themselves measured over time (e.g. "controlling for stress over time") — not to be confused with a time-constant characteristic like sex. In the multilevel model they enter as additional fixed effects; in the latent model, their effect is estimated freely at each occasion (no assumption that the effect stays constant over time).

Further advanced methods

Three additional Pro methods, operated like SEM/CFA via a confirmation card with editable variable dropdowns:

Equivalence testing (TOST)

Statistically tests whether two values are practically equivalent — unlike an ordinary test, which only tests for a difference and doesn't let you interpret a non-significant result as "equal". TOST ("Two One-Sided Tests") flips the logic: the null hypothesis is the difference, and what gets confirmed is equivalence.

• Equivalence test for 'score' between 'group' = 1 and 'group' = 2, equivalence bounds ±5.Two independent groups (design "independent")
• Equivalence test for 'before' and 'after', equivalence bounds ±0.3 in Cohen's d.Paired measurements (design "paired"), bounds either in raw units or standardised as Cohen's d

Also available: design "one_sample" (one sample against a fixed reference value). You need to set the equivalence bounds (the range around zero within which a difference still counts as negligible) yourself based on subject-matter judgement — kondeor.ai doesn't suggest automatic bounds, since that's a domain decision.

Latent class analysis (LCA)

Identifies unobserved subgroups (classes) in the data based on response patterns across several categorical variables — e.g. to derive respondent types from several yes/no or agreement questions, without defining the groups yourself in advance.

• Latent class analysis with 'item1', 'item2', 'item3', 'item4'.At least two categorical variables required

The number of classes is chosen automatically based on BIC (Bayesian Information Criterion) across several tested solutions — no need to specify K manually. The result shows a model comparison table (AIC/BIC/log-likelihood for each tested number of classes) plus the relative size of each class found. Each case's class membership can be committed straight into the dataset just like with factor/cluster analysis (see B7).

Moderated & serial mediation

Builds on simple mediation (X acts on Y through a mediator M, available from the Basis plan), but covers two more complex models:

• Moderated mediation — the indirect effect (X → M → Y) itself depends on a fourth variable W, either at the first stage (W affects how strongly X influences M) or the second stage (W affects how strongly M influences Y).
• Serial mediation — two mediators in sequence (X → M1 → M2 → Y) instead of just one.

• Moderated mediation: 'stress' acts on 'turnover intention' through 'exhaustion', moderated by 'support'.
• Serial mediation: 'training' acts on 'performance' through 'competence' and 'confidence'.

Both methods need at least 20 usable cases (complete values on all variables involved).

Time series analysis

Covers five related methods for time-ordered data — each reachable via its own keywords in the question:

• ARIMA forecast for 'revenue' over the next 6 months.Forecast with confidence interval
• Is there a structural break in 'visitor numbers'?Chow test if the timing is known, otherwise an automatic Bai-Perron multi-search for the most likely break point
• Are there unusual points in 'daily revenue'?Outlier detection, accounts for seasonality (STL/Loess residual + IQR-robust threshold)
• CUSUM test for 'production volume'.Tests parameter stability over time, no known break point required beforehand
• STL decomposition of 'visitor numbers'.Trend/seasonal/remainder components, also provides the seasonally adjusted series as input for the other four methods

AI forecast (TiRex-2 foundation model)

As a complement to the classical ARIMA analysis, an optional AI-powered zero-shot forecast is available — a pre-trained foundation model (TiRex-2) that works without any training on your own dataset. With the matching keyword in your question, both methods run one after the other, so you can compare them directly:

• AI forecast for 'revenue' over the next 6 months.Runs alongside the classical ARIMA analysis, both results are shown one below the other

Exponential Smoothing (ETS)

A standalone alternative to the classical ARIMA analysis — automatic selection of the best error/trend/season component combination (AICc-based). Unlike the TiRex comparison feature, this runs only exponential smoothing, not ARIMA as well:

• Exponential smoothing for 'revenue' over the next 6 months."Holt-Winters" or "ETS" are also recognised as phrasing

R code to reproduce your analysis

For the most common methods, you can have kondeor.ai show you the matching, ready-to-run R code — to double-check kondeor.ai's own results, or to pick up R along the way. Just ask for it as part of your question:

• Compare 'score' by 'group'. Also show me the R code.Works for any supported method, not just the t-test

The code includes the matching read-in line with your dataset's actual filename (read.csv() or haven::read_sav(), depending on the format) — load the same dataset locally in RStudio as well, and the code runs there unchanged.

Supported: t-test, ANOVA, correlation, linear regression, chi-square, descriptive statistics, Mann-Whitney U, Wilcoxon test, Kruskal-Wallis test.

Methods overview

Name the method directly, or make a general text-analysis request — kondeor.ai will then ask for column selection and method:

• Run a Grounded Theory text analysis.
• Perform a qualitative content analysis using Mayring.

These features currently require the Pro plan. Details on all methods: kondeor.at/de/portfolio/statistik-ki/methoden (opens directly in English).

Analysing multiple text columns together

Yes, kondeor.ai can do this automatically — manually copying multiple open-ended questions into one column is not necessary for the analysis itself. If multiple free-text columns are detected in the dataset, kondeor.ai actively asks before starting the analysis:

• ALLAnalyse all detected free-text columns together (cross-column analysis)
• ONLY oe2_improvementAnalyse only one specific column

Alternatively, you can name the desired column(s) directly in your first question — kondeor.ai recognises them automatically without asking. If there's only a single free-text column in the dataset, it's selected automatically.

With "ALL", texts from all selected columns are shuffled together for the analysis (not processed column by column) — this way the AI doesn't pick up artificial patterns purely from column order. Interactive review and category export (see below) have recently started supporting multiple jointly analysed text columns too — manual merging is no longer needed for any part of the workflow.

Limits: case count in text analyses

For cost and time reasons, text analyses have an upper limit on the number of text units processed for the main analysis. The dual-model reliability check no longer has a fixed cap — for larger case counts it now runs automatically in the background (see below).

For inductive methods (A, D, E, F), the category system is discovered on a representative sample for very large case counts (currently around 220 text units) and then applied to all responses — category systems typically saturate well before the full case count is reached. For sentiment (C) and predefined categories (B), this step doesn't apply, since no new category system needs to be discovered there.

Dual-model reliability check

For sentiment (C), inductive category formation (A), Mayring (E), Grounded Theory (F), and deductive category formation (B), a second, independent classification by a second AI model (Mistral) runs in the background alongside the main analysis. Comparing the two models gives you:

• Cohen's Kappa (for sentiment) or Jaccard similarity (for multi-label category assignment) as an agreement measure
• A list of the divergent cases where the two models disagreed

This works like a second, independent coder's perspective — similar to an inter-rater reliability check with two human coders, just automated.

For Grounded Theory (F), there's an additional core category comparison (selective coding): both models independently propose a core category, and kondeor.ai shows the similarity between the two descriptions as a percentage, based on an embedding comparison. This is deliberately not a formal reliability statistic like Kappa or Jaccard — just an indicator of how closely the two proposed theoretical foci align. It runs independently of the rest of the category matching, even if that isn't possible for some reason.

For larger case counts (roughly 250 text units in total or more): the check now runs automatically in the background while you can already continue working with the main analysis. A brief status line in the chat shows progress; the result (Kappa/Jaccard, plus the divergent-cases card if applicable, see below) appears automatically as a new message once the check is done — usually within a minute or a few minutes, depending on case count. A "⏹ Cancel" control right next to the status line lets you stop the background run at any time.

Interactive review & category export

After the reliability check, you can review divergent cases directly in an interactive card and decide on each one — cases where both models agreed are adopted automatically. At the end, you add the result to the dataset as new column(s) with a single click — no more download detour needed (see next chapter).

This recently started working even when you analysed multiple text columns together (selecting "ALL" or naming several columns): if a divergent case comes from one of several analysed columns, the card shows a small column tag on each case so it's clear which original question the answer came from. With only one analysed column, the card looks exactly as before, with no extra tag.

Export with multiple text columns: kondeor.ai creates a separate column per category and source column (e.g. kat_satisfaction__question1 and kat_satisfaction__question2) instead of blending both answers into one column — after all, a respondent might be categorised differently in question 1 than in question 2. With only one analysed column, the column naming stays exactly as before (kat_satisfaction, no suffix).

Missing answers are correctly exported as a true missing value (NA), not as "0" — an empty cell in question 2 doesn't mean the respondent is "not in this category", it simply means they didn't answer that question. At kondeor.ai, "0" always means: the answer was reviewed and is confirmed not to be in this category.

Writing categories back into the dataset

Clicking "Add to dataset" writes the new kat_* columns directly into this session's working dataset — no more downloading and re-uploading. The new columns are immediately usable in any follow-up analysis (e.g. a cross-tabulation right afterwards) and are automatically included the next time you use "Export dataset".

If you want to categorise several open-ended questions one after another, simply run the next text analysis and add it too — all kat_* columns accumulate in the same working dataset (alternatively: analyse several columns together in one pass, see above — then a single "Add to dataset" click is enough).

The new category columns then behave like any other nominal variable — e.g. usable for cross-tabulations or group comparisons.

Multiple response set: when you add the categories, kondeor.ai automatically bundles the kat_* columns from one categorisation into a so-called multiple response set (SPSS's equivalent: "Define Multiple Response Sets") — with several open-ended questions in the same chat, one set is created per question. For SPSS users, an additional toolbar button then appears offering a small syntax file (.sps) that creates the set once you run it in SPSS.

Technical note: SPSS files can't receive a multiple response set directly during export (a limitation of the underlying library, independent of kondeor.ai) — the syntax file needs to be run once in SPSS. To anchor the set permanently in the .sav file, re-save the file in SPSS afterwards.

Frequency analysis directly in kondeor.ai: instead of going through SPSS, just ask in the chat — e.g. "Analyse multiple responses" or "Frequency of multiple responses". kondeor.ai returns a table with count, % of responses, and % of cases per category (SPSS convention: "cases" refers to everyone with at least one valid answer in the set, not the overall N). If several sets exist in the same chat, you can name the relevant open-ended question in your request (e.g. "frequency for satisfaction"); without naming one, all existing sets are shown. This is a plain count with no AI call involved — it doesn't cost any credits.

Saving a result to a report

Under every result (quantitative or qualitative) you'll find the "Save to report" button. This lets you:

• Add the analysis to an existing report, or create a new report
• When creating a new report, optionally choose your own brand profile (see D1b) — without one, the report appears in the standard kondeor.ai design
• Optionally add a title for this specific analysis and an analyst note (a hint for the client)

A report collects multiple analyses over time this way — ideal for having a complete write-up in one place at the end of a project.

Reports (individual and combined) are currently available from the Basis plan and up.

Custom brand profiles for reports

Instead of the standard kondeor.ai design, reports and combined reports can use a custom brand profile — with your own logo, your own header/footer text, and a light or dark colour scheme for the header and footer. The rest of the report content (tables, charts) is deliberately kept consistent.

• A brand profile is created once and can then be applied to any number of your own reports and combined reports — you can create as many profiles as you like (e.g. one per client)
• A later change to the profile (e.g. a new logo) takes effect immediately on all reports that use it
• Without a logo, a cleanly typeset wordmark using your entered header text is shown instead
• The "powered by kondeor.ai" note in the footer can optionally be hidden for a one-time additional fee

Manage profiles via "🎨 Brand profiles" next to "My Reports". Creating a brand profile, as well as hiding the "powered by" note, costs credits (a one-time cost per profile, reusable without limit afterwards) — you'll see the exact amount directly in the app when creating one.

Viewing & exporting the individual report

A saved report (with all the analyses collected in it) can be viewed as a whole and exported as an HTML file — including interactive charts. For a print version or PDF, the report itself has a "🖨 Print / Save as PDF" button that uses your browser's print function with a layout specifically optimised for it (A4, clean page breaks).

A report can also be shared via a link — recipients then see a public, readable view without needing their own kondeor.ai account.

Combining several reports into a combined report

Several already published individual reports can be combined into a combined report — e.g. to bundle multiple survey waves or sub-studies of a project under one shared link. Each included report appears in it as its own, renameable tab.

• Create a new combined report via "🗄️ Combined reports" (next to "My Reports") and give it a name
• Add any number of your own published individual reports as tabs — only published reports are offered, so a shared combined report never links to a dead tab
• Reorder tabs via drag & drop and rename them individually
• The combined report gets its own, separate share link — the individual reports remain separately accessible via their own links as well

Just like the individual report, a shared "⬇ Save as HTML file" and "🖨 Print / Save as PDF" button covers all tabs at once.

If an included individual report is later set back to draft, its tab disappears from the published combined report automatically (instead of showing a broken link) — it reappears once the individual report is published again.

The management view also shows how often the combined report was opened in total, and how often each individual tab was actually clicked (not just loaded) — a simple counter, no detailed history of individual views.

Video summary

Instead of (or in addition to) text, kondeor.ai can also generate a result summary as a short presenter video — for example for a presentation in a course or in front of management. A "🎬" button appears in several places:

• After a theory answer — summarises the essence of the explained concept
• After an analysis — answers the question that led to the analysis directly
• After an analysis and its related theory request — combines both into one video
• On a project report or batch report — synthesises several analyses into one summary

Every video is designed for around 3 minutes by default — two dropdown fields next to the "🎬" button let you choose duration (30 sec. to 3 min. in five steps) and format (16:9 landscape or 9:16 portrait, e.g. for Instagram) individually. Since a presenter avatar handles formulas, exact statistical values, and Greek letters poorly, kondeor.ai rephrases the content specifically for this — results are described qualitatively (e.g. "a small, statistically significant difference") rather than read out as a number.

Before the actual video is generated, kondeor.ai shows you the generated presenter script in an editable text field — you can adjust the wording before video creation starts.

A finished video can be downloaded with one click, or saved to your personal "Videos" folder (visible only to you). Important: the video link is only valid for about 7 days via the provider — download the video if you want to keep it permanently; saving the link alone is not enough.

In a saved project report, you can additionally add a YouTube link (your own upload, or a link to a displayed kondeor.ai video), which is then embedded directly in the report.

Video usage draws on your current credit allowance (see Plan comparison) — a video costs noticeably more than a single analysis, since it's generated via an external provider.

Plan comparison

Every plan includes a monthly credit allowance (rolling 30-day window from your first login, no rollover into the next cycle). Every analysis consumes credits — how many depends on the method: a simple analysis (e.g. t-test, correlation) costs little, more elaborate procedures (e.g. regression, factor analysis) cost more, and computationally intensive methods with cross-validation or a broad model search (e.g. random forest, regularization, time series analysis) or a qualitative text analysis cost the most. An individual report (creating it, adding analyses, viewing, exporting) costs nothing; a combined report (see D1b) and creating a custom brand profile, on the other hand, cost credits — you'll see the exact numbers directly in the app when creating one. Re-displaying already-computed results also costs nothing. The credits actually charged are shown directly with each analysis result; your current balance is shown by the icon in the top header, and the exact allowances and prices per plan are always available, up to date, under "Upgrade plan" in the app.

One exception to the "no rollover" rule: if you switch between plans (upgrade or downgrade), any allowance still remaining at that moment isn't lost — it carries over into the new 30-day cycle that begins with the switch. From that new cycle onward, the normal rule applies again: anything unused expires at the next cycle's end, just like any regular monthly allowance. Purchased credits (see E5) are unaffected by this and remain valid until your plan ends regardless.

Invoices & payment

Payments are processed via Mollie (SEPA, credit card, and others); invoices are generated automatically and available for download in your account. Businesses with a valid EU VAT ID from abroad automatically benefit from the reverse-charge procedure (0% VAT, verified via VIES) — private individuals and Austrian businesses pay the standard rate of 20%.

Cancellation vs. withdrawal

These are two legally distinct things:

For details, see § 12 (Termination) and § 13 (Right of Withdrawal) of the Terms and Conditions.

Both are different from deleting your account (E7): cancelling ends your subscription but leaves your account and your data in place.

Terms, privacy & data processing

The complete legal documents (Terms and Conditions, Privacy Policy, Data Processing Agreement) are collected on a dedicated page, each available in German and English:

→ Open legal documents

If the European servers are briefly unreachable, we ask you whether the task may instead be carried out via a provider outside the EU — this only happens with your explicit consent.

Your uploaded data set is temporarily stored server-side, in encrypted form, at our hosting partner Mittwald (Germany) during the session, and is automatically deleted after 12 hours of inactivity (see G3). See Privacy Policy, point 3.3, for details.

AI-generated text (e.g. interpretations, reports, video scripts) may contain a watermark, imperceptible to humans, used for machine-readable origin verification — content and readability are unaffected. See Privacy Policy, point 3.8, for details.

Buying additional credits

If your monthly credit allowance ever runs short, Basis and Pro customers can buy additional credits via the "+ Credits" button in the header — one flexible package (100 credits), you choose the quantity yourself (1–20 packages).

The key difference from your monthly allowance: purchased credits don't expire at the end of the cycle — they remain valid for as long as your plan is active. Every analysis draws on your regular monthly allowance first; only once that's used up do purchased credits come into play.

Your billing address is pre-filled from your most recent invoice when you buy — you can override it at any time, for example with a new VAT ID or a changed address. Just like a plan purchase, every credit purchase generates a regular invoice.

Password, signing out & language

Forgotten or changing your password: the "Forgot / change password?" link in the sign-in dialog takes you to a field for your email address. You'll then receive an email containing a link that stays valid for one hour; use it to set a new password (at least 8 characters). This is also the route to take if you simply want to change your password deliberately — there is no separate form for that.

For security reasons you always get the same confirmation, whether or not an account exists for the address you entered. That way the form can't be used to find out who is registered with kondeor.ai. If no email arrives, please check your spam folder and the spelling of the address.

Signing out: the "Sign out" button at the top right of the header ends your session immediately. On top of that you are signed out automatically after 12 hours (see G3 · Session expired).

Switching language: the "DE"/"EN" button in the header switches between German and English — both the interface and the language your questions are answered in. Your choice is stored in your browser and reused on your next visit. On a first visit the language follows your browser setting. If kondeor.ai is embedded in or linked from another page, the language can also be set directly via the address suffix ?lang=de or ?lang=en.

This manual is available in both languages too — the switch for it sits at the top left of the sidebar.

Deleting your account

You can delete your account yourself at any time, no questions asked: open "Info", then click "Delete account" at the bottom left of the window. You'll be asked for your password once more — that protects you from someone else removing your account at an unattended screen.

What gets deleted: your uploaded datasets, saved analyses and projects, your question history, your credit history, plus course enrolments and learning progress. This happens immediately and cannot be undone — there is no recovery window. So export anything you want to keep beforehand (see D3).

What is kept: your invoices. Austrian law requires them to be retained for seven years (§ 132 BAO), which expressly takes precedence over the right to erasure under Art. 17 GDPR (Art. 17(3)(b)). They are stored for that purpose only and used for nothing else.

You'll receive a confirmation by email afterwards. You're welcome to register again with the same address at any time — it starts as a completely new account, so old content does not come back.

Self-service deletion is blocked in two cases: while a subscription is still running, please cancel it first (see E3) and delete the account once the paid period has ended — that way deletion never triggers a payment. And if your account owns courses with enrolled participants, please contact office@kondeor.ai so the courses can be handed over first.

Course catalog & enrolment

The "Courses" button shows all public, active courses — with the number of units, access duration (in months from enrolment), and creator. Free courses can be enrolled in with one click; paid courses lead to a payment via Mollie (one-off payment, no subscription).

Courses you're already enrolled in or have purchased are marked accordingly in the catalog and lead straight to "My Courses" instead of another payment.

My Courses: units & progress

"My Courses" shows all courses you're enrolled in, each broken down into units. Every unit has a status:

• Locked — not yet reachable, unlocks once the previous unit is passed
• Open — ready to learn
• Passed / Failed — after completing the exam, including the score achieved; if failed, another attempt is possible

Units unlock linearly — the first unit is open immediately after enrolment, every further unit only after passing the exam of the previous one.

Learning steps

Within an open unit, kondeor.ai first creates a structured learning plan (3–5 steps, tailored to the unit and an optional hint from the course instructor) and then guides you through it step by step. Each step provides a detailed explanation (roughly 200–350 words) with everyday examples, citing the course materials where possible — the same curated knowledge base used in normal theory mode (see A6). Relevant videos can be shown alongside each step.

Exam per unit

After the learning steps comes an exam — the questions can come from up to three sources at once, depending on how the instructor set up the unit: the instructor's own question pool, a curated question database (matched to the chapter), and/or AI-generated questions. Multiple-choice, true/false, and free-text questions are supported — free-text answers are graded by AI, including brief feedback on why an answer was right or wrong.

The minimum score needed to pass is set per unit by the instructor. If you fail, the unit stays open for another attempt; if you pass, the next unit unlocks automatically.

Statistical methods in a course (Free plan exception)

If you are enrolled in at least one active course, you can use all Basis-level methods (t-test, ANOVA, regression and more, see E1) with the unchanged GBOO practice dataset — even if you are on the Free plan yourself. The exception applies only to the practice dataset; as soon as you upload your own data or edit the practice dataset (change a cell, transform, filter), the regular Free-plan limits apply again for the rest of the session.

A brief note about this appears once in the chat, the first time the exception actually unlocks a method that would otherwise be locked on the Free plan.

The exception ends automatically once your course enrolment expires — after that, your regular plan applies again (Free, Basis, or Pro), with whatever credit allowance you had already used by then. To use Basis-level methods beyond the course, or with your own data, an upgrade to the Basis plan is required.

Question gets misunderstood

Rephrasing with clear context usually helps:

• I mean a new question – please ignore the last analysis.
• Refer to the regression analysis just performed.

If it's specifically about using kondeor.ai (not statistics or your data), switching to help mode (mode button next to Analysis/Theory/Power) often helps too — see A7.

Tip: if a question was misunderstood, try rephrasing it rather than resubmitting it word for word — an identical repeat often triggers the same misunderstanding again.

Free-text column not recognised as text

If an open-ended question is instead treated as a frequency count (each answer appears as its own "category"), this is usually because the request wasn't clearly recognised as a text analysis. Explicitly mention one of the following keywords in your question:

• text analysis, qualitative, content analysis, categories, code, sentiment, open-ended answer, free text, Grounded Theory

Example: instead of "What should be improved?" try "Run a text analysis: What should be improved according to the open-ended answers?"

Session expired

After 12 hours of inactivity, you're automatically logged out for security reasons — this also automatically deletes any uploaded data and unsaved changes. Just upload a dataset again to start a new session; signing in again is possible immediately.

During the session, your data set is stored exclusively server-side, in encrypted form, at our hosting partner Mittwald (Germany) — not with an external provider outside the EU.

Further help

Can't find what you're looking for? Write to us directly: office@kondeor.ai