The public is bombarded daily with reports about risk factors, many conflicting with each other, others accepted as "scientific truth" for awhile, then scientifically disproved, yet others questionable that later prove to be true. Physicians are faced with trying to make sense of those conflicting or questionable results in the scientific literature in order to guide their patients to the best possible decisions. The situation is not much easier for scientists who may waste years of their productive life, and considerable resources, basing their research efforts on what prove to be misleading earlier research findings. What this book does is to present, in non "academese" and with many examples from the general media and scientific journals, a guide to a critical reading of research reports, which, in turn, serves as a guide to researchers as to which approaches are likely to be regarded with raised eyebrows, and what they need to do to generate results that will be taken seriously. This stimulating and helpful book was written for informed consumers and physicians as well as for scientists evaluating the risk research literature or contemplating projects on risk research.

To Your Health: How to Understand What Research Tells Us about Risk

Kraemer, Helena Chmura

Lowe, Karen Kraemer

Kupfer M.D., David J.

Health, Fitness & Dieting

Oxford University Press

Evaluating Medical Tests: Objective and Quantitative Guidelines

In this book, Kraemer presents a systematic, objective methodology by which to determine the effectiveness of medical tests. She shows clearly and concisely how to define statistical terms and approaches consistently from study to study, how to stipulate statistical assumptions underlying various approaches, how to check for empirical validity and how to judge the robustness of statistical outcomes, resulting in models that integrate many different approaches and extend the strengths of each.

Politics & Social Sciences

Social Sciences

Sage Publications, Inc.

In this book, Kraemer presents a systematic, objective methodology by which to determine the effectiveness of medical tests. She shows clearly and concisely how to define statistical terms and approaches consistently from study to study, how to stipulate statistical assumptions underlying various approaches, how to check for empirical validity and how to judge the robustness of statistical outcomes, resulting in models that integrate many different approaches and extend the strengths of each.

With increased emphasis on helping readers understand the context in which power calculations are done, this Second Edition of How Many Subjects? by Helena Chmura Kraemer and Christine Blasey introduces a simple technique of statistical power analysis that allows researchers to compute approximate sample sizes and power for a wide range of research designs. Because the same technique is used with only slight modifications for different statistical tests, researchers can then easily compare the sample sizes required by different designs and tests to make cost-effective decisions in planning a study. These comparisons demonstrate important principles of design, measurement, and analysis that are rarely discussed in courses or textbooks, making this book a valuable instructional resource as well as a must-have guide for frequent reference.

How Many Subjects?: Statistical Power Analysis in Research

Blasey, Christine M.

Psychology & Counseling

See the best price to sell, buy, or rent books by Kraemer, Helena Chmura. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Kraemer, Helena Chmura

See the best price to sell, buy, or rent books by Kraemer, Helena Chmura. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

To Your Health: How to Understand What Research Tells Us about Risk

Evaluating Medical Tests: Objective and Quantitative Guidelines

Evaluating Medical Tests: Objective and Quantitative Guidelines

How Many Subjects?: Statistical Power Analysis in Research