Social Science Survey Design Expert
Quick survey design for sociology, psychology, management
Social Science Survey Design Expert
Quick survey design for sociology, psychology, management
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Description
Guides users through survey design based on research topic, target population, and other information, delivering a complete questionnaire and supporting documentation. Survey design scope includes: market research (consumer behavior, brand perception, etc.), policy evaluation (public service satisfaction, policy awareness, etc.), organizational behavior (employee satisfaction, organizational commitment, etc.), and psychometrics (attitude scales, personality scales, etc.). Three modes available: Quick Basic (for time-sensitive or simple projects), Standard Process (for regular academic research), and Deep Optimization (for high-quality publications or major projects). Advantages: ensures a three-level structure of dimension-subdimension-question to avoid omissions; easy validation for subsequent factor analysis and validity tests; reusability of dimensional frameworks across projects.
Related Skills
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Survey Design Error Control
An academic questionnaire design engine distilled from three classic works: *The Questionnaire Design Handbook*, *The Design and Evaluation of Surveys*, and *Theory, Methods, and New Trends in Questionnaire Design*. Grounded in cognitive psychology and empirical methods, it covers the full process—from defining research objectives → building a dimensional framework → drafting items → selecting response options and scales → handling sensitive questions → organizing the full questionnaire → pre-evaluation → delivery—to produce a complete questionnaire designed to prevent measurement error and support reliability and validity testing, along with design notes and an evaluation checklist. Trigger terms: questionnaire design / design a questionnaire / survey questionnaire / scale design / questionnaire evaluation / questionnaire optimization / pilot survey / cognitive interview / thesis questionnaire.
STEM PhD Topic Diagnostician
Already have a PhD dissertation topic but unsure whether it is rigorous, innovative, and feasible? This Skill is designed to diagnose existing topics—not randomly generate topics from scratch or merely polish the wording. It breaks down the research subject, core variables, mechanisms, outcome measures, and research boundaries in your topic. Based on the Chinese and English literature, experimental foundation, samples, methods, equipment, timeline, and budget you provide, it assesses the topic’s rigor, novelty, and feasibility, while identifying risks such as “innovation” based only on changing materials, confounded variables, insufficient evidence for the proposed mechanism, an uncontrolled sample matrix, and chapters that cannot build progressively. The final output is a STEM PhD Topic Health Check Report, which clearly recommends whether to: retain the topic with minor adjustments; retain the research subject while strengthening its dimensions; retain the research problem and reconstruct the subject; or stop patching and redesign the topic. It is intended for STEM PhD students in food science, chemistry, materials science, biology, basic medical sciences, environmental science, agriculture, energy, and engineering. It is especially useful for those preparing a proposal defense, revising a topic at an advisor’s request, who have already defended their proposal but lack a clear central thread, or who have existing experiments but are unsure whether they can support an entire PhD dissertation.
Research Review Assistant
An expert assistant for university-level research project reviews. Provide the materials for N projects in a given category. Each project includes three types of files: a proposal, a form-check summary or interim review form, and supporting evidence. File names may be scrambled, mismatched, or changed, and files may be DOCX, XLSX, PDF, or image files. Supporting evidence may be bundled into one file or provided as multiple separate files. This Skill then completes the following steps: ① Content-anchor alignment—extracts the project or center name, principal investigator, affiliated school, and project period from within the documents; matches the three-part file set for each project; and flags items requiring review when file names do not match their contents. ② Evidence normalization—whether bundled or separate, breaks materials into “atomic evidence items” and creates a coded ledger for each item with one of four status labels: complete, needs verification, missing, or questionable. This avoids judging the strength of the evidence by file count. ③ Three-dimensional item-by-item audit—evaluates each piece of evidence for relevance, authenticity, and completeness, then reconciles it against the commitments in the proposal and the self-reported results in the interim review form. ④ Dual-track scoring and comparative ranking—uses the five weighted criteria in Appendix 2 as the reporting standard and the five dimensions of the interim review as a reference standard. The two systems remain independent and are not combined, and all projects are ranked and grouped into tiers. ⑤ Delivers a structured review draft with “score + review comments” that can be pasted directly into the official form, and can generate an interactive scoring webpage when needed. Huali’s current standards are used by default. Users can confirm or revise the criteria, weights, and reference materials before work begins, or upload replacements. There are two confirmation checkpoints: first for alignment and the evidence ledger, then for scoring and ranking. When materials cannot be identified, the user is prompted to confirm. For project completion reviews, two additional hard requirements apply: first, the correspondence between the proposal’s expected outcomes and the documented evidence must be verified. If the evidence does not include an expected outcome listed in the proposal, the project automatically fails. Second, template issues are distinguished between using the completely wrong template, which results in failure, and partial formatting inconsistencies, which result in only a small deduction.
Information
- Version
- v5
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
