Data and statistics
Missing Data Is Not a Technical Detail: The Mechanism Decides
Missing data is not a cleanup step. The choice between deleting cases and imputing changes estimates and standard errors, and Q1 reviewers read that decision closely. Validity is governed by the assumed missingness mechanism, not by how much is missing. In one simulation, imputation error was similar under MCAR and MAR but exploded under NMAR, where missingness depends on the missing value itself.
Publishable vs Exploratory Visualization: Two Objects, Two Rule Sets
Exploratory visualization serves the analyst: fast, disposable, optimized to see. Publishable visualization serves the reader: read once, and it has to decode unaided. They are different objects, not two finish levels of one chart. And the publishing format changes interpretation: a controlled experiment found 'better' graphs read more accurately (OR 1.55) and clearly (OR 1.91) than 'normed' ones.
SEM for Multiple Mediation: When Linear Regression Stops Answering
Multiple mediation asks through which mechanism an effect operates, and the quantity of interest is the indirect effect, a product of paths. Linear regression estimates isolated paths, not the inference on that product nor simultaneous mediators. SEM estimates the whole system, absorbs latent variables and chains. For the interval, the choice of bootstrap changes the false-positive rate by a measurable amount.
Web Scraping in Academic Research: Public Is Not the Same as Collectable
That a datum sits on an open page is a statement about access, not about permission, and still less about ethics. Web scraping in research forces the distinction: terms of use, privacy expectations, and risk of harm draw the line technical accessibility ignores. A review of 367 studies using public Twitter data measured the gap: most reported no ethics approval, and informed consent was attempted in none of them.
Bibliometric analysis as empirical thesis argument
Asserting a gap by subjective reading is fragile under examination. Bibliometrics demonstrates the gap empirically and identifies the authors whose work the manuscript cannot ignore without losing credibility.
Measurement invariance in translated instruments
Group comparisons require empirical evidence of invariance at four levels. Without it, descriptive statistics hide systematic noise the methodological reviewer identifies in seconds.
Multilevel modeling: when MLM is required and when OLS suffices
ICC below 0.05 allows robust OLS; between 0.05 and 0.20 requires cluster-correction or MLM; above 0.20 MLM is mandatory. The rule methodological reviewers check before the second page.
A p-value alone won't cut it: Q1 reviewers read your results section
Q1 journals did not ban the p-value; they banned the p-value standing alone. Reviewers today open a results section looking for four elements in the minimum reporting package post-ASA 2016: effect size, confidence interval, statistical power justification, and substantive interpretation kept distinct from inferential interpretation.