Few-wave vs. continuous consumer panels: Some issues of attrition, bias and variance

Golany, Boaz; Phillips, Fred; Rousseau, John J.

doi:10.1016/0167-8116(91)90016-z

Cited by 6 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple sources of variance are held constant when the same individual is tested (the major advantage of panel data) and this benefit is sacrificed to some extent when a dynamic panel is used (Golany, Phillips, & Rousseau 1991). All of these sources of bias can be summarized under the heading of non-response bias.…”

Section: Panel Datamentioning

confidence: 99%

“…One problem with using a dynamic panel is the assumption that the "average" panelist is very similar across time periods even though the individuals in each time period are different. Multiple sources of variance are held constant when the same individual is tested (the major advantage of panel data) and this benefit is sacrificed to some extent when a dynamic panel is used (Golany, Phillips, & Rousseau, 1991). All of these sources of bias can be summarized under the heading of nonresponse bias.…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

See 1 more Smart Citation

Consumer buying behavior on the Internet: Findings from panel data

Lohse

Bellman

Johnson

2000

Journal of Interactive Marketing

351

195

View full text Add to dashboard Cite

Section: Panel Datamentioning

confidence: 99%

Section: Advantages Disadvantagesmentioning

confidence: 99%

Consumer buying behavior on the Internet: Findings from panel data

Lohse

Bellman

Johnson

2000

Journal of Interactive Marketing

351

195

View full text Add to dashboard Cite

“…Second, when no replacement, biased oversampling, or weighted results are possible or cost effective, Golany, Phillips, and Rousseau (1991) have suggested that the problem of bias can be addressed by reporting panel results separately for high attrition groups and then for the rest. They argue that young single males do sometimes constitute special market segments of special interest.…”

Section: Operational Implicationsmentioning

confidence: 98%

Toward a General Theory of Diary Panels

Toh

2009

Psychol Rep

View full text Add to dashboard Cite

This article represents an integration of findings reported in seven articles on diary panels that the authors have published, based on the AT&T database. There are four major issues involved with diary panels, to wit: respondent noncooperation involving item nonresponse and attrition, bias leading to estimation errors, mathematical artifacts involving regression toward the mean, and the conditioning effect of being observed. An integrated conceptual framework for diaries is advanced, consisting of five independent variables (questionnaire design, length of participation, level of aggregation, duration of usage, and defining usage rate with length used), four mediating variables (degree of difficulty, participation fatigue, natural mortality, and level of involvement), and six dependent variables (item nonresponse, attrition, accuracy, regression toward the mean, the conditioning effect, and estimation regression). This attempt at a general theory of diary panels is admittedly incomplete, but is meant to serve as a useful conceptual framework for further research on longitudinal studies involving record keeping and reporting.

show abstract

“…One of the variations of DEA models is the window analysis model. The window analysis method was first introduced by Golany et al [22] . This model provides an opportunity to observe the modification process of the enterprise's efficiency throughout a period of time.…”

Section: Introductionmentioning

confidence: 99%

Measuring an efficiency aggregation of medical diagnostic laboratories: A window NDEA approach

Vaezi

2023

Med. Imag. Proc. Tech.

View full text Add to dashboard Cite

The efficiency evaluation of laboratories, as one of the most significant areas of healthcare, plays a key role in the quality of laboratory management. The classic data envelopment analysis (DEA) models have overlooked intermediate products, internal interactions and dealt with analyzing the network within the “Black Box” mode. This results in the loss of important information, and at times, a considerable modification occurs in efficiency results. This article evaluated the efficiency of some selected medical diagnostic laboratories in the city of Tehran according to the network data envelopment analysis (NDEA) approach. We considered a four-stage structure with additional inputs and undesirable outputs. We obtain the labs’ performance over a period of 6 months in 2022 by the NDEA window analysis process. To this aim, a four-stage structure model of three chief medical diagnostic laboratory processes as the pre-test, the test, and the post-test is designed. We considered sustainability criteria (economic, social, and environmental) to appraise the performance of laboratories, thus helping to improve the social, economic, and environmental problems of medical diagnostic laboratories. By using the Delphi viewpoint, the criteria for efficiency evaluation are achieved. The results showed that laboratory unit No. 22 maintained the highest average overall efficiency, since the high accuracy of this unit’s laboratory results had led to many physicians recommending this unit to their patients. We found that the only laboratory unit No. 20 had a decreasing trend, as it is located in an area that abounds with administrative and educational centers. At the beginning of the exam period, then the summer holidays, and finally the wave of end-of-summer trips, a decline occurs in efficiency over the period of six months.

show abstract

Few-wave vs. continuous consumer panels: Some issues of attrition, bias and variance

Cited by 6 publications

References 13 publications

Consumer buying behavior on the Internet: Findings from panel data

Consumer buying behavior on the Internet: Findings from panel data

Toward a General Theory of Diary Panels

Measuring an efficiency aggregation of medical diagnostic laboratories: A window NDEA approach

Contact Info

Product

Resources

About