Smith, Herbert L.

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  • Publication
    Age-Period-Cohort Analysis: What Is It Good For?
    (2020-05-30) Smith, Herbert L.
    If you know when someone was born, and you know what time it is, you know how old they are. If you know how old someone is and when they were born, you know the date on which they are being observed. If you know someone’s age as of a given time, you know when they were born. These are ineluctable features of algebra (age ≡ period – cohort) and geometry, as reflected in the Lexis diagram (Chauvel 2014, 384-389). There are many ways that one can turn the problem (e.g., cohort ≡ period – age) and thus many alternative forms of observation, classification, and depiction. However, there is a strong statistical sense in which there are only two pieces of information, not three.
  • Publication
    La causalitĂ© en sociologie et dĂ©mographie. Retour sur le principe de l’action humaine
    (2012-08-27) Smith, Herbert L.
    Dans la littĂ©rature anglo-saxonne, le critĂšre de « manipulation » dans l'Ă©tude des liens de causalitĂ© est actuellement en dĂ©bat. De quoi s’agit-il ? La diffĂ©rence dans les rĂ©sultats qui correspondent Ă  un Ă©lĂ©ment observĂ© dans deux Ă©tats ne peut pas ĂȘtre considĂ©rĂ©e Ă  proprement parler comme l’« effet d’une cause », sauf quand les diffĂ©rents Ă©tats sont sujets Ă  la manipulation au sens oĂč, dans le cadre d’une expĂ©rience contrĂŽlĂ©e, l’expĂ©rimentateur peut assigner alĂ©atoirement les sujets Ă  ces Ă©tats (Ă  diffĂ©rents traitements, en pratique). Conceptuellement, l’expĂ©rience est fortement prĂ©sente dans la dĂ©finition statistique de « l’effet d’une cause » mais les scientifiques en sciences sociales ont tendance Ă  identifier comme causes des caractĂ©ristiques plus ou moins immuables, comme le sexe ou l’ñge. Dans ce contexte, le critĂšre de manipulation est un invitĂ© imprĂ©vu, voire malvenu. On peut toujours Ă©tendre la dĂ©finition d’une cause afin qu’elle convienne Ă  nos habitudes, mais si l’on se pose la question de savoir pourquoi ce langage de causalitĂ© nous intĂ©resse autant, on arrive Ă  la conclusion que, au fond, nous nous efforçons de dĂ©couvrir ce qui va se passer si nous faisons quelque chose, quand nous agissons. C’est par la capacitĂ© d’action plus que par celle de manipulation expĂ©rimentale que « les vraies causes » apparaissent ; en tout cas, les caractĂ©ristiques immuables ne devraient probablement pas ĂȘtre considĂ©rĂ©es comme des « causes ». Dans le monde social, la plupart des actions se dĂ©roulent Ă  un niveau plus Ă©levĂ© que celui de l’individu. Par consĂ©quent, nous nous trompons dans la plupart de nos « analyses causales ».
  • Publication
    Double Sample to Minimize Bias Due to Non-response in a Mail Survey
    (2009-12-01) Smith, Herbert L.
    A large study of nurses conducted in the U.S. states of California (CA) and Pennsylvania (PA) is based on two large samples: n^CA≈100,000 and n^PA≈65,000. The study was conducted by mail and had response rates of: p^CA=.27 and p^PA=.39 ;; the number of respondents is thus, respectively, : n_1^CA≈28,000 and n_1^PA≈25,000. Although there are many respondents, we must concern ourselves with the possibility of substantial bias due to non-response. In order to estimate and correct for this bias, a second random sample (n_01=1,300 in the two states combined) was drawn from among the non-respondents to the first survey. Thanks to financial incentives and, above all, a shorter questionnaire, we obtained a response rate above 90%. In each state, the two samples were combined to create a virtually unbiased double sample.