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The consequences of prejudice against accepting the null hypothesis were examined through (a) a mathematical model intended to stimulate the research-publication process and (b) case studies of apparent erroneous rejec-tions of the null hypothesis in published psychological research. The input parameters for the model characterize investigators ' probabilities of selecting a problem for which the null hypothesis is true, of reporting, following up on, or abandoning research when data do or do not reject the null hypothesis, and they characterize editors ' probabilities of publishing manuscripts concluding in favor of or against the null hypothesis. With estimates of the input parameters based on a questionnaire survey of a sample of social psychologists, the model output indicates a dysfunctional research-publication system. Particularly, the model indicates that there may be relatively few publications on problems for which the null hypothesis is (at least to a reasonable approximation) true, and of these, a high proportion will erroneously reject the null hypothesis. The case studies provide additional support for this conclusion. Accordingly, it is

The use of significance tests in science has been debated from the invention of these tests until the present time. Apart from theoretical critiques on their appropriateness for evaluating scientific hypotheses, significance tests also receive criticism for inviting misinterpretations. We presented six common misinterpretations to psychologists who work in German universities and found out that they are still surprisingly widespread – even among instructors who teach statistics to psychology students. Although these misinterpretations are well documented among students, until now there has been little research on pedagogical methods to remove them. Rather, they are considered “hard facts ” that are impervious to correction. We discuss the roots of these misinterpretations and propose a pedagogical concept to teach significance tests, which involves explaining the meaning of statistical significance in an appropriate way. 1.

This article compares the predictive ability of models developed by two different statistical methods, tree analysis and regression analysis. Each was used in an exploratory study to develop a model to make predictions for a specific marketing situation. The Statistical Methods The regression model is well known and no description is provided here. Tree analysis, however, is less well known. To add to the confusion, it has been labeled in a number o £ ways – e.g., multiple classification, multilevel cross-tabulations, or configurational analysis. Whatever the names, the basic idea is to classify objects in cells so that the objects in the cells are similar to one another yet different from the objects in other cells. Similarity is judged by the score on a given dependent or criterion variable (which differentiates this method from cluster or factor analysis, where the similarity is based only upon scores on a set of descriptive variables). Tree analysis is an extension to n variables of the simple cross-classification approach. Consider the following example: a researcher is studying the factors which determine whether a family owns two or more automobiles. He finds that income may be used to classify respondents. Illustrative results for his sample are provided in Figure 1. He then decides that the number of drivers in the family may also be important for high-income families.

This paper presents the results of an experiment aimed at investigating how different methods of viewing visual programs affect users ’ understanding. The first two methods used traditional flat and semantic zooming models of program representation; the third is a new representation that uses semantic zooming combined with blending and proximity. The results of several search tasks performed by approximately 80 participants showed that the new method resulted in both faster and more accurate searches than the other methods.

this report was facilitated by grants from National Science Foundation (GS-3050) and U.'S. Public Health Service (MH-20527-02). Although they should not be held responsible for positions espoused herein, I am very grateful to the following for providing comments on earlier drafts: Marl R. Jones, Paul Isaac, David Bakan, Timothy C. Brock, Bibb Latan6, Thomas M. Ostrom, Hanan C. Selvin, Martin Fishbein, Zick Rubin, and Richard A. Zeller

This paper describes the on-board HealthWatch system and presents important statistical analyses of transmission vibration responds to several fNGB40:NF2W30:Nf (HUMS), such as maneuver condition, order of4k2NF2J:2kNfNf frge database ofneuver condition, order of4k2NF2J:2kNfNf were recorded in two sets of fon, order of4k2NF2J:2kNfNf considered in real-time HUMS design and that certain steady state maneuvers yield a dramatically higher percentage ofreal-time HUMS design and that certain steady state maneuvers yield a dramatically higherNGJGkJN unrecognized advantages ftured that multi-axis recording ma I TRODUCTIO A significantRF--V(bRt]b[Rtn/Yu/nEfs/iSuyirn.D0p.zia1nEucnYuvDnI.DvipfrscRx-[-bRF-xW]bRtusscnYi1YD.Rx5xb5RF-]4-[Rt [1-4], yetntRF--V(bRt]b[Rtn/Yu/nEfs/iSuyirn.D0p.zia1nEucnYuvDnI.DvipfrscRx-[-bRF-xW]bRtusscnYi1YD.Rx5xb5RF-]4-[Rt by Hess et al [5] i volvi g tR5[]b4RF5] SH-60 iro-bird grou d simulatRV5x](RF5]]W[Rt wit]W[RtV5x](RF5]]W[Rtx itt]W[RtV5x](RF5]]W[Rtx]R5x gi eeri g k owledge or tVbRt d resultr tVbRt]]W[Rtx]R5xWR5x(Rt]R , facttr tV as vehicle stVbRt viro metR5]W dit] s, a d ma euveri g forces ca be expectRVb]b[RF5xbW-Rt] te expectRV u derstF5bVV4Rt]b[R observed vibratb pater s. Furt vibrR-5xb y sources of aircraft5bVV4Rt tources of ai ti flightV5]WRtircraft gi e, mai rotiR5( d tiR5(((5RF5V5]WRt5bVV4 recordigs more difficult]WRt alyze or i tb]bRF5-4- tbRF5-4-[RtF5V5]WRtbVV4RtxW latt5-4-[RtF5 A cot ui g series of flight]WRtbVV4RtxW g co ductx]WxRF(]b[Rt]WRtbVV4RtxWR5 tCeRb5([VRF(]b[Rt] refere ce dat(b d t(b(bRFWx(bRt]W tt(b(bRFWx(bRt]WRtbVV4RtxWR5x(Rt] tt alysis of vibrat dat collectof vibrat]WRtbVV4RtxWR5x(Rt]Rtyirn. ttctof vibrat]WR May 1999 o tay 199 g LaboratoRb5VW I tx-WRF[-(4Rt5 d EvaluatR (FLITE), which is a Cobra AH-1 rot(Rt]Rtyi t( ed by t, whi...

Capacity to return to work following injury or illness is often referred to as residual capacity. Issues involving residual capacity such as diminution of employability and reduction of earning capacity may be litigated in a variety of circumstances. Accurate, reliable job–related assessment of residual capacity is necessary because extent of residual capacity drives decision–making within contractual definitions of work disability. Functional capacity evaluations (FCEs) often fail to link information about individuals with job information. Physicians are often asked to perform disability evaluations without being provided with job information, which may put them at risk of embarrassment, including violating Title I (the “Employment” title) of the Americans with Disability Act. The purpose of this article is to describe five tools for generating job information, each with a long development history, as they may apply to assessment of residual capacity.

Using globalization and contingency theory, this paper develops a model of global account management (GAM). The model comprises the multinational supplier’s industry globalization drivers, the multinational customers ’ extent of globally coordinated buying, such customers ’ demand for GAM services, the supplier’s response in terms of using various aspects of GAM, and resulting possible improvement in the supplier’s performance. The paper develops six hypotheses linking these variables. Data on various aspects of these variables were collected in a survey of 191 executives in multinational companies. Two related models are estimated from these data using a structural equations method. The results support the argument that the supplier’s industry globalization drivers play a key role in affecting customers ’ demand for GAM services, and that supplier’s implementation of GAM leads to significant performance