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Dataset Comparison

Last updated: Jul 18, 2026, 7:28 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 Clean157Prolific APPROVED + all quality checks (IRI, duration >= 540s, reCAPTCHA, straightlining, auth)
2Flexible Clean253Prolific APPROVED + basic quality (all 3 IRIs + duration >= 480s)
3Prolific Accepted476All deduplicated V2 rows with Prolific APPROVED status
4All V2 Finished749Finished + 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 Mean2.87052.88972.89132.8962+0.0192+0.0208+0.0257
Barrier SD0.66760.72200.72030.7811+0.0544+0.0527+0.1135
Readiness Grand Mean3.03733.06033.09403.2057+0.0230+0.0567+0.1684
Readiness SD0.59450.66050.65170.7188+0.0660+0.0572+0.1243
Maturity Grand Mean3.06723.06893.12153.2312+0.0017+0.0543+0.1640
Maturity SD0.70740.80570.78460.8036+0.0983+0.0772+0.0962
B-R Correlation-0.3987-0.4272-0.3194-0.2552-0.0285+0.0793+0.1435
B-M Correlation-0.1655-0.2761-0.2358-0.2278-0.1106-0.0703-0.0623
R-M Correlation0.56920.69290.71440.7153+0.1237+0.1452+0.1461
Alpha Barriers0.86600.88460.88580.9071+0.0186+0.0198+0.0411
Alpha Readiness0.86880.90830.90930.9257+0.0395+0.0405+0.0569
Alpha Maturity0.82040.88150.87790.8866+0.0611+0.0575+0.0662

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 Clean15730127019.1%
Flexible Clean25357196022.5%
Prolific Accepted476105371022.1%
All V2 Finished749186563024.8%

Organization Size Distribution

Result Group<100100-499500-9991000-49995000-999910000+
Conservative Clean285216311119
Flexible Clean418236491431
Prolific Accepted8314270863956
All V2 Finished1212221191375991

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)3.0130.3902No significant difference (demographics stable)
Organization Size6.42150.9719No significant difference (demographics stable)
Profit Model3.2560.7768No significant difference (demographics stable)

Profit Model Distribution

Result GroupFor-ProfitNon-ProfitGovernment/Public Sector
Conservative Clean1112323
Flexible Clean1774234
Prolific Accepted3418253
All V2 Finished54911783

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-0.086-0.130-0.010-0.066
readiness0.5090.6120.6040.497
maturity0.1740.3310.3670.368

Large vs Small/Medium Org (Cohen’s d)

ConstructConservative CleanFlexible CleanProlific AcceptedAll V2 Finished
barriers0.4090.3130.0720.026
readiness-0.221-0.186-0.0360.015

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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