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The Impact of Survey Response Rates on Research Validity and Reliability

2024· book-chapter· en· W4402791631 on OpenAlexaff
Hamed Taherdoost, Mitra Madanchian

Bibliographic record

VenueAdvances in library and information science (ALIS) book series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsUniversity Canada West
Fundersnot available
KeywordsReliability (semiconductor)Reliability engineeringPsychologySurvey researchValidityStatisticsEconometricsComputer scienceApplied psychologyClinical psychologyMathematicsEngineeringPhysicsPsychometricsThermodynamics

Abstract

fetched live from OpenAlex

The response rate, which represents the number of individuals who answered the survey, is one of the factors that impact the validity and reliability of a research study. This chapter aims to investigate the factors that impact the response rate and acceptable values. A scoping literature review is conducted to investigate recent studies in different fields ranging from management to social science and information technology, to determine the ranges of response rates and the effect of factors to gain a higher number of responses. In business and management, the average response rate was 51.18%, while in information technology, it was 41.22%. Performance assessment studies had an average response rate of 50.83%, and in the management information system field, the overall average was 32.26%. Finally, in computer science, the average response rate was 56.55%.

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)0.082
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0820.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.010
Scholarly communication0.0000.231
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
GPT teacher head0.477
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2024
Admission routes1
Has abstractyes

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