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…isky Conditions New study contribution examining how cognitive biases (overconfidence, illusion of control, planning fallacy, belief in small numbers) affect entrepreneurs' opportunity evaluation, mediated by risk perception. Based on survey data from 77 Singaporean SME founders. Includes: - Full questionnaire (Sections A-D) with verbatim stimuli from paper appendix - 10 overconfidence items with answers verified against Singapore Yearbook of Statistics 2000 - 5 findings (F1-F5): overconfidence t-test + 4 Pearson correlations - config.py with persona-varied prompt generation (40 questions per trial) - evaluator.py with one-sample t-test and correlation-based replication testing Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Major changes from code review: - Replace Pearson correlations with OLS regressions for F2-F5, matching the paper's Table 4 analysis (simple OLS for F2, multiple OLS with controls for F3-F5) - Fix F5 finding: change from overconfidence→OE to small_numbers→OE (β=1.17, t=1.91, p<.06) matching Model 2 in Table 4 - Move shared scoring logic (parse_question_responses, compute_construct_scores, code_belief_in_small_numbers, iter_response_records) into study_utils.py - Fix D8 open-ended coding: "no additional information needed" now correctly codes as +1 (substantive belief in small numbers), not treated as missing - Make D8 prompt optional with de-biased framing to avoid inflating statistical-reasoning responses - Tighten demographics to Table 2 only (sex, race, education, business_size) - Fix compute_participant_scores → compute_construct_scores naming mismatch - Update ground_truth.json with reported_coefficient and reported_t_value fields for regression findings Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add study_013: Keh, Foo & Lim (2002) — Opportunity Evaluation under Risky Conditions
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