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Dataset Comparison

Last updated: May 15, 2026, 9:53 AM EDT

This page provides a comprehensive side-by-side comparison of all four primary result groups. Each group represents an independent dataset with progressively less restrictive quality criteria. Researchers can use this comparison to assess how data cleaning decisions affect statistical conclusions.

Sample Overview

#Result GroupNDefinition
1Conservative Clean124Prolific APPROVED + all quality checks (IRI, duration >= 540s, reCAPTCHA, straightlining, auth)
2Flexible Clean184Prolific APPROVED + basic quality (all 3 IRIs + duration >= 480s)
3Prolific Accepted339All deduplicated V2 rows with Prolific APPROVED status
4All V2 Finished598Finished + duration >= 120s (extreme speeders excluded)

Core Metrics Comparison

All descriptive statistics, reliability coefficients, and correlations are shown for each result group. The Δ column shows the difference from Conservative Clean (the strictest dataset). Deltas > 0.05 are highlighted in amber.

MetricConservative CleanFlexible CleanProlific AcceptedAll V2 FinishedΔ FlexibleΔ ProlificΔ All
Barrier Grand Mean2.84492.83802.84762.8524-0.0069+0.0027+0.0075
Barrier SD0.64450.70040.71550.7849+0.0559+0.0710+0.1404
Readiness Grand Mean3.03813.07993.10643.2022+0.0418+0.0683+0.1641
Readiness SD0.59140.65490.67710.7080+0.0635+0.0857+0.1166
Maturity Grand Mean3.04873.06203.12853.2232+0.0133+0.0798+0.1745
Maturity SD0.69070.77470.78500.7995+0.0840+0.0943+0.1088
B-R Correlation-0.3676-0.3932-0.2997-0.2799-0.0256+0.0679+0.0877
B-M Correlation-0.1435-0.2655-0.2293-0.2696-0.1220-0.0858-0.1261
R-M Correlation0.57260.68750.72140.7134+0.1149+0.1488+0.1408
Alpha Barriers0.86080.87400.88090.9084+0.0132+0.0201+0.0476
Alpha Readiness0.87700.91330.91950.9273+0.0363+0.0425+0.0503
Alpha Maturity0.82840.87840.88360.8908+0.0500+0.0552+0.0624

Survey Demographics Comparison (Qualtrics)

Role distribution and organization size breakdown for each result group, based on self-reported survey responses (Q1, Q4). These are organizational demographics from the TABS instrument. Prolific platform demographics (age, sex, ethnicity, plus prescreener fields like industry, company size, and occupation) are collected separately and can be used to cross-validate these survey responses via Prolific Participant ID.

Tech vs Non-Tech Composition

Result GroupNTechnicalNon-TechnicalOther% Tech
Conservative Clean1242698021.0%
Flexible Clean18442142022.8%
Prolific Accepted33976263022.4%
All V2 Finished598148450024.7%

Organization Size Distribution

Result Group<100100-499500-9991000-49995000-999910000+
Conservative Clean17441327815
Flexible Clean256025391124
Prolific Accepted5710048602846
All V2 Finished90183851154976

Filter Bias Analysis

This analysis tests whether stricter quality filters disproportionately exclude certain demographics. A Chi-square test for independence is computed across the four result groups for role, organization size, and profit model.

Demographic CategoryChi-Square (χ²)dfp-valueInterpretation
Role (Tech vs Non-Tech)1.2330.7462No significant difference (demographics stable)
Organization Size6.08150.9783No significant difference (demographics stable)
Profit Model2.8360.8304No significant difference (demographics stable)

Profit Model Distribution

Result GroupFor-ProfitNon-ProfitGovernment/Public Sector
Conservative Clean891817
Flexible Clean1263424
Prolific Accepted2426235
All V2 Finished43310065

Effect Size Comparison

Cohen’s d effect sizes for key group comparisons across all four result groups. This shows how effect sizes shift as the sample becomes less restrictive.

Tech vs Non-Tech (Cohen’s d)

ConstructConservative CleanFlexible CleanProlific AcceptedAll V2 Finished
barriers-0.077-0.242-0.055-0.135
readiness0.7240.7590.7130.537
maturity0.3990.4710.4780.411

Large vs Small/Medium Org (Cohen’s d)

ConstructConservative CleanFlexible CleanProlific AcceptedAll V2 Finished
barriers0.4100.3300.1100.109
readiness-0.108-0.1680.0280.066

Interpretation Guide

Metrics that remain stable across all four groups suggest robust findings that are not sensitive to data cleaning decisions. Metrics that show large deltas (highlighted in amber) between Conservative Clean and less restrictive groups warrant further investigation, as the finding may depend on sample composition.

As a rule of thumb: if a Cohen’s d shifts by more than 0.1 between Conservative Clean and All V2 Finished, the effect size may be inflated or attenuated by lower-quality responses. Similarly, if Cronbach’s α drops below 0.70 in larger samples, it may indicate that less-engaged respondents are adding noise to the scale.

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