Reviewers almost always ask why the sample is the size it is. Before data collection the tool computes the sample size required for an assumed effect; after collection it reports the smallest effect the achieved sample could have detected. Nothing is uploaded — it is a calculator.

Power is computed directly from the noncentral t, F and chi-square distributions, covering t tests, one-way ANOVA, ANCOVA, repeated measures, multiple and hierarchical regression, correlation and chi-square. Three tests specific to structural equation modelling are included and are not available in general-purpose power software: the RMSEA-based test of close fit (MacCallum et al., 1996), the inverse square root method for PLS-SEM (Kock & Hadaya, 2018) and joint-significance power for a simple mediated effect (MacKinnon et al., 2002).

The tool deliberately does not compute observed post hoc power. Power calculated after the fact from the observed effect size is a restatement of the p value (Hoenig & Heisey, 2001); when a reviewer asks about power, a sensitivity analysis is the appropriate answer, and the tool routes the user there.

Nearly every empirical paper opens with the same table: who the respondents were, what the items looked like, whether the scales held together, and how the constructs correlate. It is also where numbers are most often mistyped while being copied out of statistical output. The tool builds all four tables from a single raw survey file, and it is method-agnostic — studies that never touch structural equation modelling can use it as it is.

Cronbach's α is computed from its definition. ω (composite reliability) and AVE require loadings, so they are derived from a single-factor principal axis solution; these can differ slightly from a CFA or PLS measurement model, and the report says so and defers to the measurement model when one is reported. The square root of each AVE is placed on the diagonal of the correlation matrix so discriminant validity can be read off the same table.

Items that were never reverse-coded are a common and costly oversight, so they are flagged explicitly: a negative corrected item–total correlation, or an α that rises when the item is dropped, is reported rather than silently absorbed.

Scale development and validation papers report both analyses, which in practice means moving between two statistical packages and copying numbers by hand. The tool computes both from a single raw survey file. For the exploratory part it reports KMO (overall and per item), Bartlett's test of sphericity, and three separate grounds for deciding how many factors to retain: the eigenvalue-greater-than-one rule, parallel analysis, and cumulative variance explained. Extraction is by principal axis factoring or maximum likelihood, with varimax or promax rotation.

Parallel analysis (Horn, 1965) is the default, because comparing observed eigenvalues with the 95th percentile of eigenvalues from random data of the same size does not overestimate the number of factors the way the eigenvalue rule does. When the criteria disagree, the tool says so and leaves the decision to the researcher rather than resolving it silently.

The confirmatory part estimates the measurement model by maximum likelihood, with factor variances fixed at one so that factor correlations are read directly. It reports the usual fit indices with a 90% confidence interval for RMSEA, standardized loadings with significance tests, α, composite reliability and AVE, Fornell-Larcker and HTMT discriminant validity, and the standardized residual correlations that locate misfit. Items that were never reverse-coded appear as negative loadings — their magnitude is normal, so fit indices alone will not reveal them.

A study that measures every construct with one self-report questionnaire has to say what it did about common method bias, and reviewers regularly return manuscripts that leave the question unanswered. This tool takes the raw survey file and runs the three diagnostics that journals in the social sciences ask for.

Harman's single-factor test reports the variance explained by the first unrotated component. Full collinearity VIF regresses each construct on all the others, following Kock (2015). The marker-variable adjustment of Lindell and Whitney (2001) partials the smallest theoretically unrelated correlation out of every construct correlation and re-tests significance, so a correlation that survives only because of shared method variance becomes visible.

The output is a Method-section draft in Korean and English, with the tables, the in-text citations and the reference list already in place. The overall conclusion and the account of procedural remedies are highlighted: neither can be settled from the data alone, and both belong to the author.

Give it a SmartPLS report export, or the raw survey file together with a short description of the model, and it returns APA-formatted tables for the measurement model, discriminant validity, structural paths, indirect effects, explanatory power and out-of-sample prediction, each with the criterion values a reviewer will look for.

Estimation is performed in the tool itself: PLS-SEM and covariance-based SEM by maximum likelihood, bootstrap confidence intervals, specific indirect effects with bias-corrected intervals, and PLSpredict cross-validation. Values that violate a common threshold, and sentences that require the researcher's own judgment, are flagged rather than quietly resolved.

The tables and prose it produces are drafts: figures should be checked against the analysis software, and flagged sentences rewritten by the author.

Where structural equation modelling asks about the net effect of each variable, fsQCA asks which conditions, taken together, produce the outcome, and allows several distinct combinations to do so. Recent work in information systems and marketing increasingly reports both the symmetric and the asymmetric analysis from the same survey file.

The tool calibrates each construct into set membership with the direct method, tests every condition and its negation for necessity, builds the truth table, and minimizes it with the Quine-McCluskey algorithm. Complex, parsimonious and intermediate solutions are all reported, and the configuration table marks core and peripheral conditions in the notation of Fiss (2011).

Calibration thresholds and directional expectations are theoretical judgments rather than properties of the data, so the report leaves those passages highlighted for the author. Configurations whose consistency falls within .02 of the chosen threshold are flagged, because a small change in the threshold would change the solution.