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Health care insurance companies often conduct sample surveys of health plan members. Survey purposes include: consumer satisfaction with the plan and members’ health status, functional status, health literacy and/or health services utilization outside of the plan. Vendors or contractors typically conduct these surveys for insurers. Survey results may be used for plans’ accreditation, evaluation, quality improvement and/or marketing. This article describes typical sampling plans and data analysis strategies used in these surveys, showing how these methods may result in biased estimators of population parameters (e.g. percentage of plan members who are satisfied). Practical suggestions are given to improve these surveys: alternate sampling plans, increasing the response rate, component calculation for the survey response rate, weighted analyses, and adjustments for unit non-response. Since policy, regulation, accreditation, management and marketing decisions are based, in part, on results from these member surveys, these important and numerous surveys need to be of higher quality.

We conduct an experiment to analyze the impact of a well-established psychological construct, need for cognition, in an audit-related decision context. By simulating a basic audit sampling task, we determine whether the desire to engage in a cognitive process influences decisions made during that task. Specifically, we investigate whether an individual's need for cognition influences the quantity of data collected, the revision of a predetermined sampling plan, and the time taken to make a decision. Additionally, we examine the impact of cost constraints during the decision-making process.

Contrary to results in previous studies, we find those with a higher need for cognition sought less data than those with a lower need for cognition to make an audit sampling decision. In addition, we find that the need for cognition had no relationship to sampling plan revisions or the time needed to make an audit sampling decision. Previous studies regarding the need for cognition did not utilize incremental costs for additional decision-making information. Potentially, these costs provided cognitive challenges that influenced decision outcomes.

The City of New York was suddenly and deliberately attacked on September 11, 2001, killing thousands of people and leaving unbelievable destruction. Thirty-eight buildings and structures were destroyed or damaged, including seven buildings in the World Trade Center site completely leveled. Almost five years later, two very large contaminated buildings, Deutsche Bank at 130 Liberty Street and Fiterman Hall of Borough of Manhattan Community College, have yet to be cleaned up and demolished. Some 30 million square feet of commercial space was lost. Transportation was disrupted, including the loss of the World Trade Center PATH station, the 1/9 subway line and portions of Route 9A and Church Street. Cars were not allowed south of Canal Street for a week. For Americans this was a terrorist attack and a crime. It was a time for mourning losses and responding to disaster. There was the shock that something like this could happen. And there was more. The destruction of the WTC also posed competing environmental, economic and social threats.

This chapter reports estimates of consumers’ preferences for plans to improve food safety.

The plans are distinguished based on whether they address the ex ante risk of food borne illness or the ex post effects of the illness. They are also distinguished based on whether they focus on a public good – reducing risk of illness for all consumers or allowing individual households to reduce their private risks of contracting a food borne pathogen.

Based on a National Survey conducted in 2007 using the Knowledge Network internet panel, our findings indicate consumers favor ex ante risk reductions and are willing to pay approximately $250 annually to reduce the risk of food borne illness. Moreover, they prefer private to public approaches and would not support efforts to reduce the severity of cases of illness over risk reductions.

This study is the first research that allows a comparison of survey respondents’ choices between public and private mechanisms for ex ante risk reductions.

Background – Reliable and valid hospital nurse staffing measures are a major requirement for health services research. As the use of these measures increases, discussion is growing as to whether current nurse staffing measures adequately meet the needs of health services researchers.

Objective – This study assesses whether the measures, sampling frameworks, and data sources meet the needs of health services research in areas such as staffing assessment; patient, nurse, and financial outcomes; and prediction of staffing.

Methods – We performed a systematic review of articles from 1990 through 2007, which use hospital nurse staffing measures in original research, or which address the validity, reliability, and availability of the measures. Taxonomies of measures, sampling frameworks, and sources were developed. Articles were analyzed to assess what measures, sampling strategies, and sources of data were used and to ascertain whether the measures, samples, and sources meet the needs of researchers.

Results – The review identified 107 articles that use hospital nurse staffing measures for original research. Multiple types of measures, some of which are used more often than others and some of which are more valid than others, exist in each of the following categories: staffing counts, staffing/patient load ratios, and skill mix. Sampling frameworks range from hospital units to all hospitals nationally, with all hospitals in a state being the most common. Data sources range from small-scale surveys to national databases. The American Hospital Association Annual Survey is the most frequently used data source, but there are limitations with its nurse staffing measures. Arguably, the multiplicity of measures and differences in sampling and data sources are due, in part, to data availability. The limitations noted by other researchers and by this review indicate that staffing measures need improvements in conceptualization, content, scope, and availability.

Discussion – Recommendations are made for improvements to research and administrative practice and to data.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.

This paper considers threats to the internal validity of field studies that utilize survey data. Compared to laboratory experiments and field experiments, field surveys should be strong in realism, practical significance, and normative quality. However, there are substantial threats to internal validity that fall into the general categories of sampling and measurement. We consider these issues and how to deal with them. We pay special attention to the existence and impact of common method variance including strategies for avoiding it, methods for assessing it, and approaches to correcting for it. Our objective is to provide a road map for better use of survey methods.