@article{SchwarzReike2017,
  author    = {Schwarz, Wolfgang and Reike, Dennis},
  title     = {Regression away from the mean},
  series = {British journal of mathematical and statistical psychology / British Psychological Society},
  volume    = {71},
  journal   = {British journal of mathematical and statistical psychology / British Psychological Society},
  number    = {1},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0007-1102},
  doi       = {10.1111/bmsp.12106},
  pages     = {186 -- 203},
  year      = {2017},
  abstract  = {Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean ('egression from the mean') signature. We aim not to repeal cautionary advice against potential RTM effects, but to present a balanced view of regression effects, based on a clear identification of the conditions governing the form that regression effects take in repeated measures designs.},
  language  = {en}
}
@article{KohlerKreuterStuart2018,
  author    = {Kohler, Ulrich and Kreuter, Frauke and Stuart, Elizabeth A.},
  title     = {Nonprobability Sampling and Causal Analysis},
  series = {Annual review of statistics and its application},
  volume    = {6},
  journal   = {Annual review of statistics and its application},
  publisher = {Annual Reviews},
  address   = {Palo Alto},
  issn      = {2326-8298},
  doi       = {10.1146/annurev-statistics-030718-104951},
  pages     = {149 -- 172},
  year      = {2018},
  abstract  = {The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations.},
  language  = {en}
}