Settings #

Click the Settings button (wrench icon) in the top bar to open the settings panel. The panel has a sidebar for quick navigation between sections. Changes take effect immediately — existing results on the page update in place. All settings are saved in your browser and restored automatically on your next visit.

Use the Export and Import buttons to save your settings to a JSON file or load them from one. This is useful for sharing configurations with colleagues or transferring settings between machines. The Reset to defaults button restores all settings to their original values.

Changed settings are highlighted with a subtle background tint, and sections with changes show a dot indicator in the sidebar.

Display #

Language #

Choose the interface language: English, Russian, or Chinese. On first visit, DataSuite detects your browser’s language and picks the closest match.

Precision mode #

Controls how numbers are rounded throughout all output:

• Decimal places — fixed digits after the decimal point (default)
• Significant figures (fractional only) — counts significant digits in the fractional part only; the integer part is always preserved. This rescues small values (e.g. 0.00312 stays at 3 digits) without rounding large numbers unexpectedly.
• Significant figures — counts significant digits from the first non-zero digit regardless of magnitude. Standard in natural sciences.

Precision settings #

Three separate precision controls let you set different levels of detail for different types of output:

• Descriptives & percentages (default: 2) — means, medians, standard deviations, skewness, kurtosis, proportions, variance explained
• Statistics & coefficients (default: 3) — test statistics (t, F, W, χ²), effect sizes (Cohen’s d, r, η²), regression coefficients, factor loadings, fit indices, confidence intervals
• P-values (default: 3) — all p-value output when exact format is selected

Other display options #

• Exponential notation — when enabled, very large or very small numbers are shown in scientific notation (e.g. 1.23e-5).
• Auto-scroll — when enabled, the page scrolls to the results after each analysis.

Statistics #

Confidence level #

Sets the confidence level used across all analyses that produce confidence intervals: 90%, 95% (default), 99%, or 99.9%.

Bootstrap replications #

Number of resampling iterations for bootstrap-based calculations. Range: 10–10,000. Default: 100. Higher values give more stable estimates but take longer to compute.

Assumption test significance level #

The alpha threshold for assumption tests (normality, homogeneity of variance, sphericity, etc.). Default: 0.05. This is separate from the main significance level because assumption tests serve the opposite purpose — a higher alpha (e.g. 0.10) is more conservative, catching more violations.

Statistical thresholds #

Configurable cutoff values used for interpretation throughout the application. Each threshold set has labeled tiers:

VIF collinearity thresholds — used in regression analysis to flag multicollinearity:

• Moderate (default: 5), Severe (default: 10)

Model fit cutoffs — used in factor analysis and CFA to interpret fit indices:

• RMSEA: Excellent (0.05), Acceptable (0.08), Poor (0.10)
• CFI: Excellent (0.95), Acceptable (0.90)
• TLI: Excellent (0.95), Acceptable (0.90)
• SRMR: Excellent (0.05), Acceptable (0.08), Poor (0.10)

Common alternatives: Hu & Bentler (1999) suggest stricter cutoffs (RMSEA < 0.06, CFI/TLI > 0.95, SRMR < 0.08). Some fields use more lenient thresholds (RMSEA < 0.08 as acceptable, CFI > 0.90). Adjust these to match your discipline’s conventions.

Correlation strength bands — used to label correlation coefficients:

• Very strong (0.9), Strong (0.7), Moderate (0.5), Weak (0.3), Very weak (0.1)

These follow Cohen’s (1988) conventions widely used in psychology. Medical research and natural sciences often use different benchmarks. Evans (1996) suggests: 0.20–0.39 (weak), 0.40–0.59 (moderate), 0.60–0.79 (strong), 0.80+ (very strong).

P-value settings #

Display format #

• Exact — shows the computed p-value (e.g. 0.0312)
• Category — shows a threshold label (e.g. “p < 0.05”)
• Hidden — p-values are not displayed

Multiple comparison adjustment #

When running many tests simultaneously, p-values can be adjusted to control false positives. Choose an adjustment method:

• None (default)
• Bonferroni
• Holm
• Hommel
• Hochberg
• Benjamini-Hochberg (FDR)
• Benjamini-Yekutieli (FDR)

You can display both the original and adjusted values side by side, or replace the original with the adjusted value.

Which adjustment to use? Bonferroni is the most conservative — it minimizes false positives but can hide real effects. Holm is strictly more powerful than Bonferroni with the same guarantees, so it’s generally preferred. Benjamini-Hochberg (FDR) is a popular middle ground that controls the rate of false discoveries rather than eliminating them entirely — better for exploratory work. When in doubt, Holm or Benjamini-Hochberg are good defaults.

Significance level #

The alpha threshold for significance (default: 0.05). This controls when results are flagged as significant.

Significance formatting #

Several options can be combined:

• Bold significant p-values — significant p-values appear in bold
• Color significant p-values — significant p-values use a custom text color (configurable via hex input and color picker)
• Highlight significant p-values — significant cells get a highlighted background (enabled by default; color is configurable)
• Significance stars — test statistics receive asterisks (*, **, ***) based on significance level
• Interpretation column — adds a plain-language interpretation column to result tables

Appearance #

Table style #

Five border styles for result tables:

Font #

• Font family — choose between the system default, Arial, Times New Roman, Courier New, Georgia, or Verdana.
• Font size — system default or a fixed size (10–18 px).

These apply to the output section only — the rest of the interface keeps its default appearance.

Missing data #

Method #

• Pairwise deletion (default) — excludes cases only when they have missing values in the specific variables being analyzed. Maximizes available data for each calculation.
• Listwise deletion — excludes any case that has a missing value in any selected variable. Ensures all analyses use the same subset of complete cases.
• Imputation — replaces missing values with computed substitutes before analysis.

Imputation options #

When imputation is selected, choose the replacement strategy:

• Mean — numeric variables only; replaces missing values with the variable’s mean
• Median — numeric variables only; replaces with the median
• Mode — replaces with the most frequent value (works for both numeric and categorical variables)
• Constant — replaces with a fixed value you specify

Missing data handling is applied globally — it affects all analyses equally. You can also filter cases manually using the case filter.

Plotting #

Heatmap colors #

Three color settings control the color gradient used in correlation heatmaps and similar visualizations:

• High color (default: red #b2182b) — positive extreme
• Mid color (default: light gray #f7f7f7) — neutral center
• Low color (default: blue #2166ac) — negative extreme

Maximum point spacing #

Controls the maximum pixel distance between data points in line-based plots (default: 40 px). Lower values produce denser, smoother curves; higher values give sparser output.