This is in contrast to the results obtained by Civet and Tarits (2013) on Earth data and outlines the importance of data continuity and/or the quality of proxies in induction studies.
As a result, the quality of the proxy for aggregated mismatch can be unsatisfying in many cases.
The quality of the proxy (accuracy in prediction) degrades as the nonlinearity of the response surface increases.
As 'Big Data' and social media become more prevalent and pervasive, the quality of the proxy will undoubtedly increase.
This tool consists of three related measures: a child self-report, a parent report of their child's quality of life (proxy), and a parent-impact report[ 5].
We acknowledge that in other work, e.g., on quality of life, proxy and patient responses have correlated relatively poorly [ 11, 12].
For quality of life, proxy-ratings systematically score lower than self-ratings [ 65, 66] and correlate with caregiver burden [ 67] and staff attitudes [ 59, 68].
As reported previously, the 12-month follow-up study observed positive effects of DAISY on preventing the emergence of depressive symptoms (Cornell depression scale, primary patient outcome) and maintaining quality of life (proxy-rated QoL-AD, secondary patient outcome).
A study comparing responses from patients, physicians, nurses and significant others found that deviant scores of more than one response category were most often caused by the patients, indicating either that the quality of all proxy-derived information was poor or that patients' responses are of questionable validity [ 27].
All other variables were metric, and we therefore used a linear regression model to predict which ones influenced the depressive syndrome of the proxy, the quality of life of the proxy, and the subjective burden experienced by the proxy.
