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LPS
LPS Leadership Impact & ROI Suite Public Demo v0.3.0 · Simulated data · Not for production use
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PUBLIC SIMULATION MODE
All companies, people, responses, benchmarks, model values, impact assumptions, and ROI results are entirely fictional or simulated. This demo is not a real organizational diagnosis, a normative benchmark, or empirical validation. Please do not enter any real personal or company data.
SIMULATION
1 Overview→ → →
Step 1 · Interactive product demonstration

Explore leadership impact and economic scenarios

The demo automatically loads a fully simulated 360° example with eight fictional leaders and two survey waves. Explore index profiles, self/other views, action priorities, and an editable business case. All inputs and calculations remain local to this browser.

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Client & survey context
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Anonymity & analysis rules
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Data protection notice (GDPR / employee survey): Results for individual teams are displayed only once the configured minimum number of participants is reached, reducing the risk of identifying individuals. The survey is anonymous and does not collect directly identifying attributes. Any co-determination requirements (e.g. works council involvement) must be clarified before use.
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Leadership unit being assessed

The LEI captures leadership behavior experienced by employees. Meaningful interpretation requires multiple ratings per leader; a single response does not constitute a robust profile.

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Use at least 5 responses per leader. For wave-to-wave impact assessment, treat each measurement point as a separate project or documented survey wave.
Recommended starting point: Open the simulated index profile and select individual leaders or survey waves there.
Step 2 · Transparency of simulation data

Review the prepared example data

The public demo uses only a fixed, reproducible simulation dataset. File imports, custom surveys, project changes, and data exports are disabled in this version. A future Consultant Edition is intended for real engagements stored locally.

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Rating context (360°)
applies to new entries, CSV files without dedicated columns, and demo responses
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360-degree logic: One self-assessment per leader and wave; any number of other perspectives (supervisors, peers, direct reports, customers). The analysis automatically separates self and other ratings and calculates the self–other gap. For multi-leader benchmarking, define leaders through the demo dataset or via the CSV column leader.
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Import rater files
ZIP or individual JSON files
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Drag JSON or ZIP files here
or click to select · multiple files supported

Collect the JSON files returned by raters (from the data-entry tool), optionally bundle them in a ZIP archive, and import everything in one step. Complete project files (.json) are also recognized.

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Quick entry & demo

Enter a single response using the questionnaire below, generate a sample for the current perspective, or load the complete 360° demo dataset (8 leaders · 5 roles · 2 waves).

Alternative import: CSV (from third-party systems)

If data from an existing survey tool is available as CSV. Columns F1…F38, optional team,role,leader,wave.

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Recorded responses
0 responses
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No responses recorded yet.
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Questionnaire — 38 items · 7 dimensions
0/38
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5-point scale: 1 = does not apply at all/never · 3 = partly/partly · 5 = fully applies/always. Items marked INV are reverse-coded (low = positive) and are automatically recoded.
Please answer all 38 items to save the response.
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No analyzable data yet. Record at least 5 responses.
Step 4 · Economic impact

Business case & ROI of leadership development

Translates the measured LEI gap into an economic value scenario. All assumptions are editable and explicitly treated as scenario inputs; the calculation quantifies potential value, not a guaranteed return.

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Methodological context for the client discussion: The LEI, validity, and impact values shown in demo mode are simulated; monetization additionally relies on transparent conversion assumptions. A robust business case requires a measurable business pain, an economically relevant target group, an intervention that is actually implemented, and impact testing using pre/post or comparison-group data.
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Parameters: company, KPI conversion, program costs
all values editable
Company & cost parameters
KPI conversion per LEI point
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Index parameters by dimension
λ · economic leverage · controllability · planned improvement → contribution to ΔLEI
Dimensionλ 2nd-orderecon. leveragecontrollabilitycurrent scoreplanned Δweightcontribution ΔLEI
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Results overview & scenarios
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Value waterfall
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Calculation logic & assumption transparency
Step 5 · Methodological foundation

Methodology & demonstration evidence

Methodological demonstration of the intended testing and reporting logic. All sample, fit, reliability, validity, and loading values shown below are simulated demonstration values. They do not come from a real field study and must not be interpreted as empirical validation.

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Global model-fit indices SIMULATED
Acceptance criteria based on Hu & Bentler (1999) · Brown (2015)
χ²/df
1.89
✓ < 3.0
CFI
0.962
✓ > .95
TLI
0.958
✓ > .95
RMSEA
0.048
✓ < .06
SRMR
0.052
✓ < .08
✅
In the simulated demonstration dataset, all five fit indices meet the specified acceptance criteria. This explicitly does not constitute empirical confirmation of the instrument. RMSEA 90% CI [0.041, 0.055] · χ²(659) = 1247.3, p < .001 · Δ CFI vs. one-factor model: +0.127.
α
Reliability & construct validity SIMULATED
DimensionαCRAVEAssessment
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Second-order loadings (β on LEI)
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All seven dimensions load significantly on the global factor “Leadership Effectiveness” (β > .70, p < .001). In the demonstration dataset this illustrates the intended three-level measurement architecture; confirmation with real field data is still outstanding.
λ
Standardized factor loadings (first-order) SIMULATED
λ > .70 strong · .55–.70 acceptable · < .55 weak
ItemDim.ContentINVλSER²
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Discriminant validity — HTMT matrix SIMULATED
Criterion: HTMT < 0.85 (Kline, 2015)
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In the simulated demonstration dataset, HTMT values are below the specified threshold. Empirical testing of discriminant validity with real field data is still outstanding.
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CFA step by step — methodology appendix

Confirmatory factor analysis tests whether the theoretically specified structure (38 items → 7 dimensions → 1 global factor) is consistent with the observed response data. Unlike exploratory factor analysis, item-to-dimension assignments are specified in advance and their empirical support is then evaluated.

Measurement model
xi = λi · ξ + δi
Each observed item xᵢ is explained by the latent dimension ξ (with loading λᵢ) and a measurement error δᵢ.
Composite Reliability & AVE
CR = (Σλi)² / ((Σλi)² + Σθi)    ·    AVE = Σλi² / n
CR > .70 and AVE > .50 indicate that the items measure their dimension reliably and share sufficient common variance.

The heterotrait–monotrait ratio compares correlations across different dimensions with correlations within dimensions. Values < 0.85 indicate that the dimensions capture distinct constructs rather than redundant ones.

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Hu, L. & Bentler, P. M. (1999). Structural Equation Modeling, 6(1). · Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). · Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Estimation: R · lavaan 0.6-17 · robust MLR.
Step 6 · Simulated results report

Sample management report

The report demonstrates structure, synthesis, and action logic using fictional data. Download, print, and Office exports are disabled in the public demo and are intended only for a licensed Consultant Edition.

Export disabled in public demo Discuss an enterprise solution
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