Response and nonresponse

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All (141) (130 to 140 of 141 results)

131. Basic ideas of multiple imputation for nonresponse Archived
Articles and reports: 12-001-X198600114439
Description:
Multiple imputation is a technique for handling survey nonresponse that replaces each missing value created by nonresponse by a vector of possible values that reflect uncertainty about which values to impute. A simple example and brief overview of the underlying theory are used to introduce the general procedure.
Release date: 1986-06-16
132. The maximum likelihood method for non-response in sample surveys Archived
Articles and reports: 12-001-X198600114441
Description:
The analysis of survey data becomes difficult in the presence of incomplete responses. By the use of the maximum likelihood method, estimators for the parameters of interest and test statistics can be generated. In this paper the maximum likelihood estimators are given for the case where the data is considered missing at random. A method for imputing the missing values is considered along with the problem of estimating the change points in the mean. Possible extensions of the results to structured covariances and to non-randomly incomplete data are also proposed.
Release date: 1986-06-16
133. Some aspects of nonresponse adjustments Archived
Articles and reports: 12-001-X198500114359
Description:
Unit and item nonresponse almost always occur in surveys and censuses. The larger its size the larger its potential effect will be on survey estimates. It is, therefore, important to cope with it at every stage where they can be affected. At varying degrees the size of nonresponse can be coped with at design, field and processing stages. The nonresponse problems have an impact on estimation formulas for various statistics as a result of imputations and weight adjustments along with survey weights in the estimates of means, totals, or other statistics. The formulas may be decomposed into components that include response errors, the effect of weight adjustment for unit nonresponse, and the effect of substitution for nonresponse. The impacts of the design, field, and processing stages on the components of the estimates are examined.
Release date: 1985-06-14
134. Characteristics of respondent and non-respondent households in the Canadian Labour Force Survey Archived
Articles and reports: 12-001-X198200114329
Description:
This article presents findings from a study to characterize responding and non-responding households in the Labour Force Survey (LFS). This study was motivated by two projects associated with the LFS Redesign, namely, the family estimation project and evaluation of non-response compensation procedures. However, the results of the study are of general interest in the assessment of the quality of data emanating from the LFS.
Release date: 1982-06-15
135. Rotation group bias in the Labour Force Survey (LFS) estimates Archived
Articles and reports: 12-001-X198200114330
Description:
The paper attempts to evaluate the impact of non-response adjustment by rotation groups on rotation group bias in the estimates from the Canadian Labour Force Survey. Results on bias and non-response characteristics are presented and discussed. An index used to measure rotation group bias is given and some empirical results are analyzed.
Release date: 1982-06-15
136. The nonresponse problem Archived
Articles and reports: 12-001-X198100214320
Description:
This paper presents an outline of the nonresponse research which is carried out at the Netherlands Central Bureau of Statistics. The phenomenon of nonresponse is put into a general frame-work. The extent of nonresponse is indicated with figures from a number of CBS-surveys. The use of auxiliary variables is discussed as a means for obtaining information about nonrespondents. These variables can be used either to characterize nonrespondents or as stratification variables in adjustment procedures.
Adjustment for nonresponse bias by means of subgroup weighting is considered in more detail. Finally, the last section lists a number of other methods which also aim at reduction of the bias.
Release date: 1981-12-15
137. The measurement and magnitude of nonresponse in U.S. consumer telephone surveys Archived
Articles and reports: 12-001-X198000254944
Description:
Due to the absence of hard data and the lack of standardization with respect to nonresponse terminology and reporting procedures, U.S. commercial survey researchers have been unable to obtain an accurate assessment of the nature and extent of the nonresponse problem. However, the results of two recent studies conducted by the author among leading U.S. based market and public opinion research firms revealed that nonresponse is one of the major problems now confronting the commercial survey research industry. This paper discusses the results of the two studies and their implications.
Release date: 1980-12-15
138. Causes of incomplete data, adjustments and effects Archived
Articles and reports: 12-001-X198000254945
Description:
The article provides a general overview of the concepts of incomplete data and non-response. It is recognized that non-response is an important indicator of data quality, as it affects the estimators by introducing bias and increasing variance due to a reduction in the effective sample size. The relationship between bias and the non-response rate is less obvious, since it depends on the extent of non-response and on the difference in the various characteristics between respondents and non-respondents.
The most effective way of dealing with the effects of non-response is to minimize its extent. However, any attempt to control the extent of non-response must be based on a good understanding of its origins. The causes and extent of non-response are fundamentally related to (i) the type of survey, (ii) the data capture methods, and (iii) the sample design. However, given a sample design, the extent of non-response will be influenced by factors such as the type of region and the type of non-response.
There are several ways to handle incomplete data. Each one, in the end, assigns a value to the missing or incorrect data, unless it is decided to publish “raw” data. The procedure for assigning values is called imputation and such an imputed value presumably describes the characteristic of the non-respondent.
The article provides a brief philosophical explanation about validation and imputation and their applications in the methodology of the various imputation procedures. These include weighting, replication, hot deck imputation using previous data and substitution by a zero value. The using of imputation compared with the methods used in the Canadian Labour Force Survey (LFS) is also discussed. A decision table is provided indicating the various steps to follow for a particular case of a partially completed LFS questionnaire.
Release date: 1980-12-15
139. Non-response in the Canadian Labour Force Survey Archived
Articles and reports: 12-001-X197900100002
Description: This paper includes a description of interviewer techniques and procedures used to minimize non-response, an outline of methods used to monitor and control non-response, and a discussion of how non-respondents are treated in the data processing and estimation stages of the Canadian Labour Force Survey. Recent non-response rates as well as data on the characteristics of non-respondents are also given. It is concluded that a yearly non-response rate of approximately 5 percent is probably the best that can be achieved in the Labour Force Survey.
Release date: 1979-06-15
140. Non-response and imputation Archived
Articles and reports: 12-001-X197800254830
Description:
The problems of dealing with non-response at various stages of survey planning are discussed with implications for the mean square error, practicality and possible advantages and disadvantages. Conceptual issues of editing and imputation are also considered with regard to complexity and levels of imputation. The methods of imputation include weighting, duplication, and substitution of historical records. The paper includes some methodology on the bias and variance.
Release date: 1978-12-15

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Analysis (140)

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51. Evaluation and treatment of non-response in the ELFE cohort: Results of the pilot studies Archived
Articles and reports: 11-522-X200800010960
Description:
Non-response is inevitable in any survey, despite all the effort put into reducing it at the various stages of the survey. In particular, non-response can cause bias in the estimates. In addition, non-response is an especially serious problem in longitudinal studies because the sample shrinks over time. France's ELFE (Étude Longitudinale Française depuis l'Enfance) is a project that aims to track 20,000 children from birth to adulthood using a multidisciplinary approach. This paper is based on the results of the initial pilot studies conducted in 2007 to test the survey's feasibility and acceptance. The participation rates are presented (response rate, non-response factors) along with a preliminary description of the non-response treatment methods being considered.
Release date: 2009-12-03
52. Multi-mode data collection: What can still be expected? Archived
Articles and reports: 11-522-X200800010975
Description:
A major issue in official statistics is the availability of objective measures supporting the based-on-fact decision process. Istat has developed an Information System to assess survey quality. Among other standard quality indicators, nonresponse rates are systematically computed and stored for all surveys. Such a rich information base permits analysis over time and comparisons among surveys. The paper focuses on the analysis of interrelationships between data collection mode and other survey characteristics on total nonresponse. Particular attention is devoted to the extent to which multi-mode data collection improves response rates.
Release date: 2009-12-03
53. Indicators for the representativeness of survey response Archived
Articles and reports: 11-522-X200800010976
Description:
Many survey organizations use the response rate as an indicator for the quality of survey data. As a consequence, a variety of measures are implemented to reduce non-response or to maintain response at an acceptable level. However, the response rate is not necessarily a good indicator of non-response bias. A higher response rate does not imply smaller non-response bias. What matters is how the composition of the response differs from the composition of the sample as a whole. This paper describes the concept of R-indicators to assess potential differences between the sample and the response. Such indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. Some practical examples are given.
Release date: 2009-12-03
54. Company-centric communication approaches for business survey response management Archived
Articles and reports: 11-522-X200800010983
Description:
The US Census Bureau conducts monthly, quarterly, and annual surveys of the American economy and a census every 5 years. These programs require significant business effort. New technologies, new forms of organization, and scarce resources affect the ability of businesses to respond. Changes also affect what businesses expect from the Census Bureau, the Census Bureau's internal systems, and the way businesses interact with the Census Bureau.
For several years, the Census Bureau has provided a special relationship to help large companies prepare for the census. We also have worked toward company-centric communication across all programs. A relationship model has emerged that focuses on infrastructure and business practices, and allows the Census Bureau to be more responsive.
This paper focuses on the Census Bureau's company-centric communications and systems. We describe important initiatives and challenges, and we review their impact on Census Bureau practices and respondent behavior.
Release date: 2009-12-03
55. Prioritizing business respondents to target important non-response Archived
Articles and reports: 11-522-X200800010984
Description:
The Enterprise Portfolio Manager (EPM) Program at Statistics Canada demonstrated the value of employing a "holistic" approach to managing the relationships we have with our largest and most complex business respondents.
Understanding that different types of respondents should receive different levels of intervention and having learnt the value of employing an "enterprise-centric" approach to managing relationships with important, complex data providers, STC has embraced a response management strategy that divides its business population into four tiers based on size, complexity and importance to survey estimates. Thus segmented, different response management approaches have been developed appropriate to the relative contribution of the segment. This allows STC to target resources to the areas where it stands to achieve the greatest return on investment. Tier I and Tier II have been defined as critical to survey estimates.
Tier I represent the largest, most complex businesses in Canada and is managed through the Enterprise Portfolio Management Program.
Tier II represents businesses that are smaller or less complex than Tier I but still significant in developing accurate measures of the activities of individual industries.
Tier III includes more medium-sized businesses, those that form the bulk of survey samples.
Tier IV represents the smallest businesses which are excluded from collection; for these STC relies entirely on tax information.
The presentation will outline:It works! Results and metrics from the programs that have operationalized the Holistic Response Management strategy.Developing a less subjective, methodological approach to segment the business survey population for HRM. The project team's work to capture the complexity factors intrinsically used by experienced staff to rank respondents. What our so called "problem" respondents have told us about the issues underlying non-response.
Release date: 2009-12-03
56. Non-response in a random digit dialling survey: The experience of the General Social Survey's Cycle 21 (2007) Archived
Articles and reports: 11-522-X200800010994
Description:
The growing difficulty of reaching respondents has a general impact on non-response in telephone surveys, especially those that use random digit dialling (RDD), such as the General Social Survey (GSS). The GSS is an annual multipurpose survey with 25,000 respondents. Its aim is to monitor the characteristics of and major changes in Canada's social structure. GSS Cycle 21 (2007) was about the family, social support and retirement. Its target population consisted of persons aged 45 and over living in the 10 Canadian provinces. For more effective coverage, part of the sample was taken from a follow-up with the respondents of GSS Cycle 20 (2006), which was on family transitions. The remainder was a new RDD sample. In this paper, we describe the survey's sampling plan and the random digit dialling method used. Then we discuss the challenges of calculating the non-response rate in an RDD survey that targets a subset of a population, for which the in-scope population must be estimated or modelled. This is done primarily through the use of paradata. The methodology used in GSS Cycle 21 is presented in detail.
Release date: 2009-12-03
57. Using paradata to manage nonresponse in the Survey of Labour and Income Dynamics Archived
Articles and reports: 11-522-X200800010996
Description:
In recent years, the use of paradata has become increasingly important to the management of collection activities at Statistics Canada. Particular attention has been paid to social surveys conducted over the phone, like the Survey of Labour and Income Dynamics (SLID). For recent SLID data collections, the number of call attempts was capped at 40 calls. Investigations of the SLID Blaise Transaction History (BTH) files were undertaken to assess the impact of the cap on calls.The purpose of the first study was to inform decisions as to the capping of call attempts, the second study focused on the nature of nonresponse given the limit of 40 attempts.
The use of paradata as auxiliary information for studying and accounting for survey nonresponse was also examined. Nonresponse adjustment models using different paradata variables gathered at the collection stage were compared to the current models based on available auxiliary information from the Labour Force Survey.
Release date: 2009-12-03
58. Effects of number of call attempts on nonresponse rates and nonresponse bias: Result from some case studies at Statistics Sweden Archived
Articles and reports: 11-522-X200800010999
Description:
The choice of number of call attempts in a telephone survey is an important decision. A large number of call attempts makes the data collection costly and time-consuming; and a small number of attempts decreases the response set from which conclusions are drawn and increases the variance. The decision can also have an effect on the nonresponse bias. In this paper we study the effects of number of call attempts on the nonresponse rate and the nonresponse bias in two surveys conducted by Statistics Sweden: The Labour Force Survey (LFS) and Household Finances (HF).
By use of paradata we calculate the response rate as a function of the number of call attempts. To estimate the nonresponse bias we use estimates of some register variables, where observations are available for both respondents and nonrespondents. We also calculate estimates of some real survey parameters as functions of varying number of call attempts. The results indicate that it is possible to reduce the current number of call attempts without getting an increased nonresponse bias.
Release date: 2009-12-03
59. Keeping in touch with project participants between surveys: A mailing experiment Archived
Articles and reports: 11-522-X200800011000
Description:
The present report reviews the results of a mailing experiment that took place within a large scale demonstration project. A postcard and stickers were sent to a random group of project participants in the period between a contact call and a survey. The researchers hypothesized that, because of the additional mailing (the treatment), the response rates to the upcoming survey would increase. There was, however, no difference between the response rates of the treatment group that received the additional mailing and the control group. In the specific circumstances of the mailing experiment, sending project participants a postcard and stickers as a reminder of the upcoming survey and of their participation in the pilot project was not an efficient way to increase response rates.
Release date: 2009-12-03
60. Is there really any benefit in sending out introductory letters in Random Digit Dialling (RDD) surveys? Archived
Articles and reports: 11-522-X200800011001
Description:
Currently underway, the Québec Population Health Survey (EQSP), for which collection will wrap up in February 2009, provides an opportunity, because of the size of its sample, to assess the impact that sending out introductory letters to respondents has on the response rate in a controlled environment. Since this regional telephone survey is expected to have more than 38,000 respondents, it was possible to use part of its sample for this study without having too great an impact on its overall response rate. In random digit dialling (RDD) surveys such as the EQSP, one of the main challenges in sending out introductory letters is reaching the survey units. Doing so depends largely on our capacity to associate an address with the sample units and on the quality of that information.
This article describes the controlled study proposed by the Institut de la statistique du Québec to measure the effect that sending out introductory letters to respondents had on the survey's response rate.
Release date: 2009-12-03

Reference (1)

Reference (1) ((1 result))

1. Labour Force Survey Response Rates, September 2023
Surveys and statistical programs – Documentation: 75-005-M2023001
Description: This document provides information on the evolution of response rates for the Labour Force Survey (LFS) and a discussion of the evaluation of two aspects of data quality that ensure the LFS estimates continue providing an accurate portrait of the Canadian labour market.
Release date: 2023-10-30

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Date modified:: 2024-11-04