58 Decision Trees and Pracitcal Templates for Selecting and Justifying Any Quantitative Test with Confidence
“Which test do I use?” It is the single most common question asked by graduate students and clinical researchers. You buy expensive, 800-page statistics textbooks that explain the math, but you're still left guessing which button to click in SPSS, R, or Stata.
This workbook is the bridge between statistical theory and analytical execution. It provides a visual, decision-tree approach to test selection, ensuring you choose the right test for your data type, distribution, and research question every single time.
Perfect for: Students conducting quantitative dissertations • Researchers who feel "statistically anxious" • PhD candidates preparing for a defense or peer review • Clinical investigators needing to justify their analytic plan
Eliminate confusion by mapping out the precise types and roles (IV, DV, covariate, moderator) for every variable in your study.
Step-by-step diagnostic worksheets for normality (Shapiro-Wilk, Q-Q plots), homogeneity of variance (Levene's), linearity, and multicollinearity.
Annotated guides for SPSS, JASP, and R showing you exactly which numbers to report for t-tests, ANOVAs, regressions, chi-squares, and their non-parametric alternatives.
Fill-in-the-blank results paragraphs ensuring your final write-up meets strict academic and peer-review publication standards.
Stop guessing which statistical test to use. Start analyzing with confidence.