This book addresses a broad array of pressing challenges of longitudinal surveys and provides innovative solutions to methodological problems based on the example of the NEPS. It covers longitudinal issues such as sampling, weighting, recruiting and fieldwork management, the design of longitudinal surveys and the implementation of constructs, conducting competence tests over the life course, effective methods to improve and to maintain the highest level of data quality, data management tools for large-scale longitudinal surveys, the dissemination of research data to heterogeneous scientific communities, as well as establishing a long-term public relations and communications unit integrating a study's stakeholder community over time.
Passar bra ihop. Big choice! Four major issue areas emerged from the workshop: 1 theoretical issues, 2 methodological issues, 3 adequacy of data, and 4 dissemination of data.
The topics represent the participants' cross-cutting observations and reflect the issues and challenges researchers face. This book addresses a broad array of pressing challenges of longitudinal surveys and provides innovative solutions to methodological problems based on the example of the NEPS. It covers longitudinal issues such as sampling, weighting, recruiting and fieldwork management, the design of longitudinal surveys and the implementation of constructs, conducting competence tests over the life course, effective methods to improve and to maintain the highest level of data quality, data management tools for large-scale longitudinal surveys, the dissemination of research data to heterogeneous scientific communities, as well as establishing a long-term public relations and communications unit integrating a study's stakeholder community over time.
The framework is used to evaluate the effects of different designs in the "Improving Survey Measurement of Income and Employment" study. The article also clarifies the causes of longitudinal inconsistencies in repeated panel surveys seam effects and discusses the extent to which dependent interviewing can reduce these. The article ends with issues yet to be resolved.
UK households: a longitudinal study - Wikipedia
Seam bias refers to the tendency for estimates of change measured across the "seam" between two successive survey administrations to far exceed change estimates measured within a single interview — often by a factor of 10 or more. The authors describe such procedures and examines their impact, but even with the improvement, much seam bias still remains. Pantoja-Galicia1, M. Thompson and M.
Kovacevic, "Assessing the Temporal Association of Events using Longitudinal Complex Surveys": The authors begin with an overview of a notion of temporal order along with a formal nonparametric test for a partial order relationship presented by Thompson and Pantoja-Galicia Estimation of the densities involved in the test is discussed, taking account of the interval censoring nature of the data. The complexities of the survey design are also incorporated.
Methodological issues of longitudinal surveys : the example of the national panel study
An important element of the nonparametric test is the standard error which is then assessed. The two final sections present the applications.
The authors close with discussion and possible extensions. The model is shown to correct flows in the expected direction: estimated true transition rates exhibit higher mobility than observed ones. In addition, the measurement part of the model has significant coefficient estimates, and the estimated response probabilities show a clear, sensible pattern.
Our approach provides a means of accounting for correlated classification errors across panel data which is less dependent on multiple indicators than previous formulations of latent class Markov models van de Pol and Langeheine ; Bassi et al. First, evidence from previous research that has modeled the response process within a multivariate framework is reviewed.
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Following other recent treatments in the literature, the estimation model treats survey participation as involving two sequential events, contact and response. Couper and Mary Beth Ofstedal, "Keeping in Contact with Mobile Sample Members": The authors first offer an overview of the location problem, as distinct from other sources of panel attrition or nonresponse. They then offer a framework for understanding the location propensity to guide fieldwork strategies and identify likely sources of bias.
Then they briefly describe two cases studies, examining the extent of the location problem, and correlates of non location.
They end with a brief discussion of the changing role of technology in facilitating or impeding the ability to locate sample persons who have moved or changed contact information, and offer some thoughts for research and practice. The methodological approach is different from the majority of previous studies in this area in that there is no attempt to estimate biases in marginal and associational distributions through comparison with a fresh cross-sectional sample.
They refer to this as the cognitive stimulus CS hypothesis. Specifically, they use a range of empirical indicators to evaluate the theory that repeatedly administering attitude questions serves to stimulate respondents to reflect and deliberate more closely on the issues to which the questions pertain. This, in turn, results in stronger and more internally consistent attitudes in the later waves of a panel.
It is also a useful tool for research testing and evaluation. The interviews are conducted by telephone or are self-administered by mail or web. After a description of the Gallup Poll Panel, the author analyzes the effect of three experiments on panel attrition. The experiments are a long recruit survey introduction versus a shorter introduction, a signature on the part of the recruited member when they return the initial intake questionnaire versus no signature and a comparison of attrition for those members assigned to a totally self-administered survey process versus those members that are interviewed by telephone on every other survey contact.
In preparation for the examples, the preceding section provides an overview of types of interventions, types of effects, some issues in the design and analysis of evaluation studies, and the value of longitudinal data. Eideh and Gad Nathan, "Joint Treatment of Nonignorable Dropout and Informative Sampling for Longitudinal Survey Data": The authors study, within a modeling framework, the joint treatment of non-ignorable dropout and informative sampling for longitudinal survey data, by specifying the probability distribution of the observed measurements when the sampling design is informative.
The proposed method combines two methodologies used in the analysis of sample surveys: for the treatment of informative sampling and informative dropout.
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One incorporates the dependence of the first order inclusion probabilities at the initial time period on the study variable, see Eideh and Nathan , while the other incorporates the dependence of the probability of nonresponse on unobserved or missing observations, see Diggle and Kenward An empirical example based on British Labour Force Survey data illustrates the methods proposed. This includes longitudinal research where subjects are followed for many years, but also access panels whose members agree to respond to a certain number of questionnaires only when their information is needed.
Related Methodological Issues of Longitudinal Surveys: The Example of the National Educational Panel Study
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