Data Analysis (etp-data-analysis)

Data Analysis (etp-data-analysis)

When to trigger

• The estimator is chosen but endogeneity, selection, or survivorship is not yet addressed in the numbers
• You used TWFE/OLS on staggered or time-varying venture data without checking for bias
• A time-to-event outcome (founding, exit, failure, IPO) is modeled with a linear regression
• A reviewer asks for robustness, an alternative specification, or an IV/control-function
• Qualitative coding needs an analysis plan a methods reviewer will accept

The ETP analysis bar

ETP wants analysis that the theory can stand on and that survives the new-venture inference traps. Because the journal is method-plural, "analysis" differs by branch — but every branch must (a) match the estimator to the outcome and the entrepreneurial data structure, (b) confront endogeneity/selection head-on, and (c) report uncertainty honestly. ETP house style follows APA: report effect sizes and confidence intervals, not a forest of significance asterisks standing in for substance.

Branch paths

Quantitative — outcome-appropriate estimation

• Time-to-event (founding, exit, failure, IPO): use survival / event-history (Cox, discrete-time hazard, competing risks). Modeling "did it exit (0/1)" with OLS throws away timing and censoring information.
• Counts / rare events (patents, hires, funding rounds): negative binomial / zero-inflated where overdispersion or excess zeros bite, not OLS.
• Bounded / proportion outcomes (survival rate, equity share): fractional/beta models, not naive linear.
• Panel with staggered timing (policy/financing shocks across cohorts): beyond TWFE — Callaway–Sant'Anna, Sun–Abraham — with a clean event-study and pre-trend evidence.

Endogeneity and selection (the ETP reflex)

• Selection into founding / survival: Heckman / control-function when the sample conditions on success; report the exclusion restriction's logic.
• IV: strong first stage; with weak instruments use weak-IV-robust inference; defend exclusion in institutions and theory, not just statistically.
• Reverse causality (does growth cause financing or vice versa): lagged designs, shocks, or dynamic panel (system-GMM) with instrument-count discipline.

SEM / measurement models

• Report CFA fit (CFI, RMSEA, SRMR), composite reliability, AVE, and discriminant validity (HTMT) for entrepreneurial constructs; test common-method bias when self-report dominates (marker variable, not just Harman's single factor).

Qualitative analysis

• A transparent coding scheme, the Gioia data structure as an exhibit, inter-coder agreement where appropriate, and traceability from quotation → code → theoretical dimension. The output is a process model, not a code count.

Make the magnitude mean something for practice

ETP's dual mandate reaches the results: translate coefficients into the venture-relevant scale (a hazard ratio as "ventures with X fail 30% faster," a marginal effect as "one more co-founder shifts funding probability by Y points"). A practitioner implication needs a magnitude, not a p-value.

Checklist

• Estimator matches the outcome type (hazard for time-to-event; count/fractional models where appropriate)
• Selection/survivorship addressed in the analysis, not just acknowledged
• Endogeneity strategy stated with a defended exclusion/identification logic
• Staggered designs use modern DID with pre-trend evidence (no naive TWFE)
• SEM: fit indices, reliability, AVE, discriminant validity, CMB test reported
• Qualitative: data structure, coding transparency, quotation traceability
• Effects reported with magnitudes and CIs (APA), translated for practice

Anti-patterns

• Linear regression on a time-to-event outcome (ignores censoring and timing)
• Selection/survivorship acknowledged in prose but absent from the model
• Asterisk theater — significance stars substituting for effect sizes and CIs
• Naive TWFE on staggered venture/policy data with no heterogeneity-bias check
• Harman's single factor offered as if it settled common-method bias
• Code counts presented as if they were a process theory

Worked vignette (illustrative)

A team wants to test whether accelerator participation raises venture survival, using cohorts admitted across several years and a binary "survived to year 3" outcome. The first draft runs OLS on the 0/1 outcome with year and region controls. Three ETP-specific upgrades: (1) the outcome is fundamentally time-to-event — recast as a discrete-time hazard or Cox model with competing risks (acquired vs. shut down vs. still operating), recovering the timing and censoring OLS discards; (2) accelerators select promising ventures, so survival differences may be selection, not treatment — exploit a plausibly exogenous admission threshold (a scoring cutoff supports a regression-discontinuity or fuzzy-RD design) rather than controls alone; (3) because cohorts enter in staggered years and the program changed over time, a naive two-way fixed-effects "treatment" coefficient can be biased — use a modern staggered-DID estimator with a pre-trend check. Finally, report the hazard ratio with a CI and translate it: "admitted ventures fail roughly 25% slower over three years," a magnitude an accelerator director can act on.

Output format

【Journal】Entrepreneurship Theory and Practice
【Branch】quantitative / SEM / qualitative
【Outcome→estimator】outcome type + matched model
【Selection/survivorship】how addressed in the numbers
【Endogeneity】IV / control-function / lagged / dynamic panel + exclusion logic
【Inference】effect sizes + CIs (APA); CMB if self-report
【Magnitude for practice】coefficient translated to venture scale
【Next skill】etp-contribution-framing