These questions focus on the experience of all previous demographic groups but also have a natural language processing components to them. One could approach these programmatically with word cloud analysis after some natural language sanitization related to stop word removal, punctuation removal, and capitalization standardization. Similarly, one could measure the sentiment of the different responses given and compare across groups.
Of technologists who plan to leave their roles or have left, what is a synopsis of the reasons they provide?
Of Black technologists who plan to leave their roles or have left, what is a synopsis of the reasons they provide?
Of Latina technologists who plan to leave their roles or have left, what is a synopsis of the reasons they provide?
Of technologists who are parents who plan to leave their roles or have left, what is a synopsis of the reasons they provide?
Of technologists who are parents who plan to leave their roles or have left, what is the breakdown of interventions that might have stopped them?
Of technologists who plan to stay in tech, what is a synopsis of the reasons they provide?
Of Black technologists who plan to stay in their roles, what is a synopsis of the reasons they provide?
Of Latina technologists who plan to stay in their roles, what is a synopsis of the reasons they provide?
Of technologists who plan to stay in their roles, what is a synopsis of the reasons they provide?
These questions focus on the experience of all previous demographic groups but also have a natural language processing components to them. One could approach these programmatically with word cloud analysis after some natural language sanitization related to stop word removal, punctuation removal, and capitalization standardization. Similarly, one could measure the sentiment of the different responses given and compare across groups.