Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  conducted semantic transparency rating experiments using both the traditional laboratory-based method and the crowdsourcing-based method. Then  compared the rating data obtained from these two experiments.  observed very strong correlation coefficients for both overall semantic transparency rating data and constituent semantic transparency data (rho > 0:9) which means the two experiments may yield comparable data and crowdsourcing-based experiment is a feasible alternative to the laboratorybased experiment in linguistic studies.  also observed a scale shrinkage phenomenon in both experiments: the actual scale of the rating results cannot cover the ideal scale [0;1], both ends of the actual scale shrink towards the center. Hover, the scale shrinkage of the crowdsourcing-based experiment is stronger than that of the laboratory-based experiment, this makes the rating results obtained in these two experiments not directly comparable. In order to make the results directly comparable,  explored two data transformation algorithms, z-score transformation and adjusted normalization to unify the scales.  also investigated the uncertainty of semantic transparency judgment among raters,  found that it had a regular relation with semantic transparency magnitude and this may further reveal a general cognitive mechanism of human judgment.
['Shichang Wang', 'Chu-Ren Huang', 'Yao Yao', 'Angel Chan']
I am writing a research paper on a idea Mechanical Turk-based Experiment vs Laboratory-based Experiment: A Case Study on the Comparison of Semantic Transparency Rating Data
[{"from": "human", "value": "I am writing a research paper on a idea Mechanical Turk-based Experiment vs Laboratory-based Experiment: A Case Study on the Comparison of Semantic Transparency Rating Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  conducted semantic transparency rating experiments using both the traditional laboratory-based method and the crowdsourcing-based method. Then  compared the rating data obtained from these two experiments.  observed very strong correlation coefficients for both overall semantic transparency rating data and constituent semantic transparency data (rho > 0:9) which means the two experiments may yield comparable data and crowdsourcing-based experiment is a feasible alternative to the laboratorybased experiment in linguistic studies.  also observed a scale shrinkage phenomenon in both experiments: the actual scale of the rating results cannot cover the ideal scale [0;1], both ends of the actual scale shrink towards the center. Hover, the scale shrinkage of the crowdsourcing-based experiment is stronger than that of the laboratory-based experiment, this makes the rating results obtained in these two experiments not directly comparable. In order to make the results directly comparable,  explored two data transformation algorithms, z-score transformation and adjusted normalization to unify the scales.  also investigated the uncertainty of semantic transparency judgment among raters,  found that it had a regular relation with semantic transparency magnitude and this may further reveal a general cognitive mechanism of human judgment."}]