1. A computer implemented method of predicting the validity of subject reported data, comprising the steps of: a) analyzing validity markers wherein said validity markers comprise a subject's timeliness in responding to a question or responding to a prompt, a subject's length of response to a question, consistency of a subject's response with other collected data, proximity of data on a subject's response to an expected data range, completeness of data, a subject's voice stress levels, historical information on a subject's veracity, failure of a subject to complete a survey, a subject's response to a question about morale, a subject's preference of a particular good, service or media outlet, a survey environment's temperature, a survey environment's light level, a survey environment's ambient noise level, a subject's heart rate, a subject's blood pressure, a subject's temperature, a subject's skin electroconductivity, a subject's perspiration, or a subject's rate of blinking; b) analyzing historical protocol data wherein said historical protocol data comprises data on the research protocol of an earlier clinical trial or survey, an earlier question posed to a subject, frequency of prompting of a subject during various times of the day or week, time allowed for a subject to respond to a question, requirements of a subject's behavior, conditions mandating removal of a subject from certain statistical analyses, or conditions mandating removal of a subject as a participant in a clinical trial or survey; c) generating at least one predictive algorithm for predicting the invalidity of subject reported data by quantitative analysis of said validity markers and said historical protocol data; and d) translating said at least one predictive algorithm into at least one prediction rule for use with a survey.