File Drawer Research Articles

Einstein: disagreement delays publication of collected works

David Dickson reports on the circumstances that have delayed the publication of Albert Einstein's collected writings, which fill 28 file drawers at Princeton University

The file drawer problem and tolerance for null results.

For any given research area, one cannot tell how many studies have been conducted but never reported. The extreme view of the drawer problem is that journals are filled with the 5% of the studies that show Type I errors, while the file drawers are filled with the 95% of the studies that show nonsignificant results. Quantitative procedures for computing the tolerance for filed and future null results are reported and illustrated, and the implications are discussed. (15 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)

The file drawer problem and tolerance for null results.

The file drawer problem and tolerance for null results.

WATER DATA ‐ A NEW AWARENESS1

ABSTRACT: One of the most significant changes m the field of hydrology in the past few years has been the increase m demand for basic data resulting from a new awareness on the part of planners, developers and managers of the essential nature of such data. For many years data collection has been an onerous, routine operation, following which the data were processed and stored ‐ either in publications or file drawers ‐ and the job considered completed. Two developments have changed that picture: the realization that we are drastically altering OUT environment, and the advent of the computer. The first forced us into a recognition of our need for accurate basic data and the second provided a new methodology for handling and using it. The change is evidenced m many ways and numerous activities are underway at both State and Federal level for all facets of the acquisition and handling of water data. The collection of basic data still involves hard routine work and a conscientious‐effort to maintain a high level of quality. Hopefully, recognition of the absolutely essential nature of an adequate data base will result in the continued enhancement of the basic data collector and the concomitant increase in support of his activities.

Moral Man and Immoral Academy?

Weeding through a file drawer other day I ran across something I'd written in halcyon days of my academic adolescence when I was young and tenured. It was, I flinched at recalling, a challenge flung in faces of my peers in academy to view nature of our professional role as an opportunity to witness in and out of classroom to our existential commitments. It announced, among other things, that academy possessed the freedom to encourage its faculties to select them if need be to push student beyond professionally dictated confines of his discipline so that student approaches ultimacy in its cognitive forms: in an examination of [a given] dicipline's epistemological, and thus ontological, presuppositions; in following of a given proposition to its ultimate implication; in perceiving potential relatedness of all separate wares hawked in academic marketplace. It went on to suggest that the scholar must draw himself within circle, must accept engagements within class as well as without, must acknowledge and act upon existential roots of his own intellectual structures. He must . . .witness to his own confrontation with ultimacy so that cognitive wares he sells may be confronted in their entirety [and] deny that clean lines of efficiency, however admirable they may be, are next to godliness. Rather, he is commanded to witness to integrity of I-Thou in

Kidney Disease in the Young

This inexpensively published book, written by a wise and well-known nephrologist, is a useful compendium of hard-to-obtain statistics, history, references, theoretical and practical considerations specifically related to pediatric kidney diseases. The volume is composed of a series of reviews of varying length and depth, some of which are busy rather than informative. Perhaps in an attempt to give the series of brief chapters some coherence, a rather conversational tone has been adopted from time to time, as if some of the chapters were dictated and not revised for uniform style. The data, many of the references, and some of the technical approaches to diagnosis are from previous generations of medical practice. The classical and exemplary content of many cases and references does not vitiate their importance or usefulness, but one has the feeling that file drawers are being emptied, issue by issue, so that every conceivable problem within the limits

The Charles F. Kettering Archives

gaps can be filled with the recollections of close associates. To assemble the archival material, appropriate files were scanned for Kettering-related materials. Historical materials at the headquarters of the Charles F. Kettering Foundation and elsewhere were tapped. Oral history interviews were conducted with those who were associated with Kettering in engineering, scientific, medical, and other endeavors, as well as with many of his personal friends. A significant portion of the oral histories has already been recorded and transcribed. Over half of the file drawers have been indexed by a retired technology librarian, resulting in some 8,000 index cards. Special storage facilities are under construction.'

A Filing Problem

n samples are drawn from a known distribution, ordered and placed in groups inside file drawers with a previously decided number of samples going in each drawer; the initial (ordered) sample in a drawer defines the “beginning” of a drawer, and the initial (ordered) sample of the next drawer defines the “end” of the drawer under consideration. If N new samples are drawn from the same distribution, what is the probability that x of them fall “inside” a given filing drawer? Is it shown that this probability depends only on the number of original samples “inside” a drawer, and is independent of the original sampling distributions and of the original sample which defines the beginning of the drawer. Hence, if the design objective is to equalize the overflow probability of each drawer, the best a priori policy is to divide the original ordered samples equally among the file drawers.