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.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. ETP is entrepreneurship, where selection and survival bias are pervasive — foreground identification and selection corrections.

• Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg — report the adjusted threshold.
• OVB sensitivity: oster_delta / sensemakr.
• Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley; multilevel data → cluster at the right level.
• Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each.
• Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.