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Introduction
PhD statistical what‑if data analysis teaches you to question assumptions and test robustness in your dissertation work. Firstly, you learn why exploring alternative scenarios uncovers hidden biases. Moreover, the content demonstrates how small parameter tweaks alter results meaningfully. Additionally, frequent transition words guide you smoothly between concepts. Consequently, you build confidence in your analytical decisions. Meanwhile, practical examples illustrate each step clearly.
Scenario‑Based Model Switching
Furthermore, scenario‑based model switching compares different statistical models side by side. Firstly, you run linear regression, logistic regression, and generalized additive models on identical data. Moreover, visual overlays highlight effect size changes across models. Additionally, summary tables show how assumptions shift parameter estimates. Consequently, you identify the model best aligned to your research question. Meanwhile, expert tips guide criterion selection.
Parameter Sensitivity Testing
Moreover, parameter sensitivity testing examines how minor input changes affect outputs. Firstly, you adjust confidence levels, sample weights, and priors in Bayesian analyses. Furthermore, sensitivity plots reveal outcome variability under each tweak. Additionally, diagnostic tables list parameters that drive instability. Consequently, you learn to set robust thresholds proactively. Meanwhile, best‑practice advice ensures meaningful interpretations.
Data Preparation Variations
Additionally, data preparation variations showcase the impact of preprocessing choices. Firstly, you compare analyses with and without outliers. Moreover, mean substitution and multiple imputation methods are evaluated side by side. Furthermore, stratified versus cluster sampling is contrasted in common research scenarios. Consequently, you see how each decision shapes final results. Meanwhile, reproducible code snippets support experimentation.
Assumption Testing & Correction
Furthermore, this section focuses on diagnosing and correcting assumption violations. Firstly, tests for normality, homoscedasticity, and autocorrelation are demonstrated. Moreover, bootstrapping, robust errors, and transformations are applied live. Additionally, simulations show how corrections change outcomes. Consequently, you report both original and adjusted results transparently. Meanwhile, ethical considerations guide responsible disclosure.
Reporting What‑If Findings
Finally, learn to document what‑if analyses effectively in your thesis. Firstly, structured templates show where to place exploratory checks. Moreover, sample language clarifies speculative versus confirmatory insights. Additionally, side‑by‑side figure layouts compare base‑case and alternative results. Consequently, your dissertation communicates robustness persuasively. Meanwhile, published thesis examples demonstrate best practices.
This case study explores three “What If” sensitivity analyses on Likert-scale data in a Public Health dissertation—covering threshold effects, continuous modeling, and implications for research validity.
This case study showcases how “What If” scenario analysis using R can improve the impact and scalability of quasi-experimental educational interventions in PhD research.
Purpose Likert scale what if analysis was used to gauge how definitional choices for “High Intent” impact conclusions, three scenario
