Survey Research Designs: Towards a Better Understa. Questo articolo non è disponibile.Survey Research Designs: Towards a Better Understa

ROBERT F. BORUCH AND ROBERT W. PEARSON During the 13th Century. a vigorous argument among Middle Eastern rabbis concerned how one ought to make a fair assessment of an olive crop value for tithing purposes. Should one consolidate the crop. systematically mix the olives. and then conscientiously draw a small random sample? Or. might one simply grab a handful of olives from the nearest basket and make an estimate of the crop's worth. The issue. of course. is one that research designers and research users confront often -- balancing the need for information against the resources that must be put into actually collecting the data -- in deciding how much effort is warranted to produce fair evidence. For the rabbis. who argued for twenty-five years over the matter. the issue can be resolved with a special rule. In this as in other cases. if the demand for information is biblical in origin -- if God is its source. then one ought to be considerably conscientious; a more scientific and more expensive endeavor is warranted. We may at times subscribe to this kind of rule of thumb in determining what quality of information is warranted under what conditions. But other rules and approaches are possible. And as medieval Jewish. Arabic. and Christian philosophy suggests. the alternatives need to be thought out and tested. Our interest is a bit more contemporary but has some spiritual kinship with early scholars' interest in empirical evidence.

Research Designs and Causal Inferences: On Lord’s Paradox.- 1. Introduction.- 2. A Model for Causal Inference.- 2.1 The elements of the model.- 2.2 Discussion of the elements of the model.- 2.3 Three kinds of studies.- 2.4 Causal effects and related quantities in causal studies.- 3. Lord’s examples.- 3.1 Example 1: Two subpopulations receiving one treatment.- 3.2 Example 2: A descriptive study.- 3.3 Example 3: Contemplating new treatments.- 3.4 Example 4: Two explicit treatments.- 4. Discussion.- Appendix: Randomization and Inference for Causal Effects.- A.1 The completely randomized experiment.- A.2 Causal effects in subpopulations.- A.3 Randomization within populations.- A.4 Randomization based on a covariate.- A.5 Randomization based on a covariate within subpopulations.- A.6 Descriptive studies.- References.- Tables and Figures.- Toward Conducting Benefit-Cost Analysis of Data Programs.- 1. An Illustration of the Use of Benefit-Cost Analysis.- 2. A Closer Look at Benefit-Cost Analysis.- 3. Data Quality.- 4. Benefits.- 5. Uses of Data in Decisionmaking.- 6. Nonoptimal Data Use.- 7. Summary and Conclusions.- Tables and Figures.- The Design and Analysis of Longitudinal Surveys: Controversies and Issues of Cost and Continuity.- Abstract.- 1. Introduction.- 2. Conceptual Framework.- 3. The Key Controversy: It’s All a Matter of Perspective.- 4. Costs and the Effects of Scale.- 5. Longitudinal Missingness, Attrition, and Imputation.- 6. Drawing Formal Inferences from Sample Survey Data.- 7. Summary and Answer to Questions.- References.- The Role of Panel Studies in a World of Scarce Research Resources.- 1. Analytic Benefits of Panel Designs.- 1.1. The role of panel data in describing and explaining change.- 1.2. Panels as successive cross-sections.- 2. Panel Designs and Data Quality.- 2.1. Effects of memory on response error.- 2.2. Effect of panel participation.- 2.3. Analytic costs of “cheaper” panel studies.- 3. Relative Monetary Costs of Longitudinal Studies.- 4. Summary.- References.