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Record W4283582660 · doi:10.1080/26408066.2022.2086443

Sexual Violence Among Postsecondary Students: No Evidence that a Low Response Rate Biases Victimization or Perpetration Rates in a Well-Designed Climate Survey

2022· article· en· W4283582660 on OpenAlexafffund
Nicole Jeffrey, Charlene Y. Senn, Michelle A. Krieger, Anne Forrest

Bibliographic record

VenueJournal of Evidence-Based Social Work · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Windsor
FundersCanada Research Chairs
KeywordsCensusRepresentativeness heuristicStratified samplingAmerican Community SurveySurvey data collectionNon-response biasSelection biasGeographyIncentiveSample (material)Survey methodologyPsychologyDemographyStatisticsSocial psychologySociologyEconomicsMathematicsPopulation

Abstract

fetched live from OpenAlex

Purpose The purpose of this study was to assess the accuracy and representativeness of common census-sampled campus climate surveys given the potential for misestimating sexual violence (SV) rates on campuses due to low response rates and self-selection bias in research (mixed findings in previous research).Method We compared SV rates obtained from a census-sampled campus climate survey with a lottery draw (a common method for collecting campus SV data) with those obtained from a gender-stratified random sample survey with individual incentives.Results We found no evidence that census-sampled campus climate surveys misestimate SV: our low response rate census-sampled survey produced very similar rates to our high response rate random sample survey.Discussion and Conclusion Our research suggests that less costly and labor-intensive census-sampled surveys, when well-designed, produce sufficiently accurate and representative SV estimates on campuses despite their lower response rates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0030.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.169
GPT teacher head0.407
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes2
Has abstractyes

Explore more

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