Comparing Single-Item and Multi-Item Trust Scales: Insights for Assessing Trust in Project Leaders
Bibliographic record
Abstract
The purpose of this research is to provide researchers and leaders with a reliable and up-to-date comparison between a single-item and a multi-item trust scale, enabling effective assessment of team members' trust in their leaders. The aim of the study is to investigate whether a single-question scale is as reliable as a multi-item questionnaire in measuring trust. An additional goal is to provide researchers with insights and conditions for effectively using single or multiple measures to assess trust in leaders, considering factors like reliability and effectiveness. After conducting a comprehensive literature review, data were collected from 101 project members in Brazil using a survey methodology. The respondents were asked to provide feedback regarding their leaders, specifically project managers, and factor analysis was then employed to test the single-item and multi-item measures of trust. The advantages and disadvantages of each approach are discussed. The findings of our study demonstrate that both single-item and multi-item scales of trust should be utilized to gain a more comprehensive understanding of the trust construct. Single-item questionnaires can reduce survey length, improve respondent friendliness, and increase participant willingness. On the other hand, multi-item questionnaires enable researchers to analyze latent variables that contribute to an overall variable, but they cannot isolate data for each of those constructs. The results show that both measures are reliable, providing researchers and professionals with insights into the benefits and drawbacks associated with each method. Consequently, this research equips researchers and project professionals with valuable information for selecting the appropriate measurement tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".