smr-literature-positioning

SMR Literature Positioning

Use this to place the contribution in the methods literature, not the substantive one. SMR reviewers are methodologists who often know the closest prior estimator, the original derivation, and the competing approach in a neighboring discipline. A missed precedent is the fastest path to a reject.

Position against methods, not findings

The literature review of an SMR paper answers "what is the closest method, and how is yours different?" — not "what is known about the substantive topic." Structure the review as a small map:

• Direct ancestors: the method(s) your contribution extends, corrects, or replaces. Cite the primary source, not a textbook summary.
• Cross-discipline siblings: the same problem solved in statistics, econometrics, psychometrics, machine learning, or network science. SMR readers expect you to know that a "new" sociology method may already exist under another name elsewhere.
• Competing methods: the alternatives a reviewer will demand you beat in simulation. Name them here so the comparison set in smr-simulation-studies is not a surprise.

The precedent audit

Run this before drafting the review:

• Search the original statistical literature, not just sociology — many SEM, latent-variable, multilevel, missing-data, and causal-inference tools originate in statistics/psychometrics.
• For each near-neighbor, write one line: what they do, what you do differently, why the difference matters. If you cannot state the difference, you have not yet differentiated the contribution.
• Distinguish re-derivation from novelty: presenting a known result as new is a credibility killer at a methods venue. If you rediscovered something, say so and add what is genuinely new.

Cross-field translation table

Sociology framing	Likely prior literature to check	Risk if missed
Causal effect with controls	statistics (potential outcomes, DAGs), econometrics	"good/bad controls" already settled elsewhere
Latent classes / trajectories	psychometrics, biostatistics (finite mixtures, GBTM)	reinventing a named model
Measurement invariance	psychometrics (MGCFA, alignment)	overstating a known non-invariance result
Missing data	statistics (MI, IPW, FIML), biostatistics	ignoring the standard estimator
Network effects	network science, spatial econometrics	a known identification problem
Text-as-data	NLP, computational linguistics, comp. social science	a method already standard in CSS

Sibling-journal misattribution guard

When citing "where methods like this are published," be precise:

• Sociological Methodology (ASA annual) ≠ Sociological Methods & Research (SAGE). Do not conflate them in the cover letter or positioning.
• Psychological Methods (APA), Political Analysis, Structural Equation Modeling, and the Journal of Educational and Behavioral Statistics are neighbors, not SMR. Attribute exemplar papers to the correct venue (see resources/exemplars/library.md).

Checklist

• Direct method ancestors are cited from primary sources, not textbooks.
• Cross-discipline siblings (statistics/psychometrics/econometrics/CSS) were searched and cited.
• Each near-neighbor has a one-line "what they do / what you do / why it matters."
• The simulation comparison set is foreshadowed in the review.
• No known result is presented as novel; re-derivations are flagged.
• Sibling journals are attributed correctly; no SMR/Sociological Methodology conflation.

Anti-patterns

• Substantive review in a methods paper: paragraphs on the topic instead of on the method.
• Sociology-only search: missing the statistics/psychometrics origin of the method.
• Textbook citation for a primary result: citing a handbook instead of the derivation.
• Strawman map: omitting the strongest competitor so the simulation looks favorable.
• Venue confusion: attributing a Sociological Methodology or Psychological Methods paper to SMR.

Output format

[Direct ancestors] <method -> your difference>
[Cross-discipline siblings] <field : closest prior + difference>
[Competing methods to beat] <named alternatives for the simulation>
[Precedent risks] <any near-rediscovery to disclose>
[Next SMR skill] smr-derivation-and-properties