Tore Dalenius

Minimum Variance Stratification

Abstract When estimating the mean value of a quantity x, in a population to be divided into L strata according to the value of a quantity closely correlated with x, it is necessary to choose the L — 1 points of stratification. Nearly optimum points are obtained if they are chosen to equalize the integrals over the various strata of the square root of the population density. A simple method for the iterative improvement of the points is given and illustrated on several examples.

An Assessment of Research-Doctorate Programs in the United States: Mathematical and Physical Sciences.

1 Front Matter 2 I. Origins of Study and Selection of Programs 3 II. Methodology 4 III. Chemistry Programs 5 IV. Computer Science Programs 6 V. Geoscience Programs 7 VI. Mathematics Programs 8 VII. Physics Programs 9 VIII. Statistics/Biostatistics Programs 10 IX. Summary and Discussion 11 Minority Statement 12 Appendix A: Letter to Institutional Coordinators and Accompanying Survey Form (Measures 01-03) 13 Appendix B: Survey of Earned Doctorates (Measures 04-07) 14 Appendix C: Letter to Evaluators and Specimen of the Instrument Used in the Reputational Survey (Measures 08-11) 15 Appendix D: The ARL Library Index (Measure 12) 16 Appendix E: Data on Faculty Research Support and R&D Expenditures (Measures 13 and 14) 17 Appendix F: Data on Publication Records (Measures 15 and 16) 18 Appendix G: Conference on the Assessment of Quality of Graduate Education Programs--Participants and Summary 19 Appendix H: Planning Committe for the Study of the Quality of Research-Doctorate Programs 20 Appendix I: Region and State Codes for the United States and Possessions

The Development of Sample Surveys of Finite Populations

This paper discusses selected topics in the theory and methods of sample surveys which have as their objectives to provide inferences about some characteristics of a finite population (as distinguished from inferences about a causal system which may have produced certain characteristics of the finite population). The paper gives an account of some early developments, considers criteria to guide the choice of sample design, and considers various aspects of probability-sampling designs, including nonsampling error and total survey design. Finally, the paper discusses the role of models in the theory and practice of sample surveys for making inferences about a finite population, and lists some areas for research and development.

THE SURVEY STATISTICIAN'S RESPONSIBILITY FOR BOTH SAMPLING AND MEASUREMENT ERRORS

The author fears that survey research is on the verge of becoming divided into content-wise independent subdisciplines and consequently he blows the whistle. Thus, he formulates a goal for the survey research community, viz. the creation of a unified discipline of survey research, to serve as the basis for total survey design. Moreover, he outlines a two-phase program for the endeavors necessary if this goal is to be reached.

Sample-Dependent Estimation in Survey Sampling

This paper considers a problem which typically confronts survey statisticians, viz. how to cope in a sample survey with uncertainty concerning a parameter of crucial importance to the choice of estimator of a parameter . The paper considers an approach—sample-dependent estimation—characterized by using the sample itself to resolve the problem.

Some Theory of Sampling When the Stratification is Subject to Error

In this paper, we will present sampling theory pertinent to the following type of sample design. Each one of the B boxes is shaken and classified into one of L strata on the basis of the auditory observation made concerning the quality of the content. A sample of boxes is selected from each stratum, and an accurate observation is made by inspecting the content of each box selected for the sample. The efficiency of thii design will depend upon the accuracy of the auditory observation, and also on the costs involved.