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All (18)

All (18) (18 of 18 results)

  • Articles and reports: 12-001-X198600214447
    Description:

    The Canadian Health and Disability Survey, administered as a supplement to the Canadian Labour Force Survey in October 1983, collected data on potentially disabled persons by means of a screening questionnaire and a follow-up questionnaire for those screened-in. The data from the screening questionnaire, consisting of a set of activities of daily living, were used to group respondents according to identifiable characteristics. A description of the groups of respondents is provided along with an evaluation of the methods used in their determination. An incompletely ordered severity scale is proposed.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214448
    Description:

    The seasonal adjustment of a time series is not a straightforward procedure particularly when the level of a series nearly doubles in just one year. The 1981-82 recession had a very sudden great impact not only on the structure of the series but on the estimation of the trend- cycle and seasonal components at the end of the series. Serious seasonal adjustment problems can occur. For instance: the selection of the wrong decomposition model may produce underadjustment in the seasonally high months and overadjustment in the seasonally low months. The wrong decomposition model may also signal a false turning point. This article analyses these two aspects of the interplay between a severe recession and seasonal adjustment.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214445
    Description:

    A problem of estimating monthly movements in rents based on data collected every four months is explored. Five alternative composite estimators of the rent index are presented and justified, both from an intuitive as well as theoretical point of view. An empirical study testing and comparing the proposed methods is described and summarized. Recommendations are put forth.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214450
    Description:

    From an annual sample of U.S. corporate tax returns, the U.S. Internal Revenue Service provides estimates of population and subpopulation totals for several hundred financial items. The basic sample design is highly stratified and fairly complex. Starting with the 1981 and 1982 samples, the design was altered to include a double sampling procedure. This was motivated by the need for better allocation of resources, in an environment of shrinking budgets. Items not observed in the subsample are predicted, using a modified hot deck imputation procedure. The present paper describes the design, estimation, and evaluation of the effects of the new procedure.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214451
    Description:

    The Canadian Census of Construction (COC) uses a complex plan for sampling small businesses (those having a gross income of less than $750,000). Stratified samples are drawn from overlapping frames. Two subsamples are selected independently from one of the samples, and more detailed information is collected on the businesses in the subsamples. There are two possible methods of estimating totals for the variables collected in the subsamples. The first approach is to determine weights based on sampling rates. A number of different weights must be used. The second approach is to impute values to the businesses included in the sample but not in the subsamples. This approach creates a complete “rectangular” sample file, and a single weight may then be used to produce estimates for the population. This “large-scale imputation” technique is presently applied for the Census of Construction. The purpose of the study is to compare the figures obtained using various estimation techniques with the estimates produced by means of large-scale imputation.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214449
    Description:

    Nearly all surveys and censuses are subject to two types of nonresponse: unit (total) and item (partial). Several methods of compensating for nonresponse have been developed in an attempt to reduce the bias associated with nonresponse. This paper summarizes the nonresponse adjustment procedures used at the U.S. Census Bureau, focusing on unit nonresponse. Some discussion of current and future research in this area is also included.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214462
    Description:

    In the presence of unit nonresponse, two types of variables can sometimes be observed for units in the “intended” sample s, namely, (a) variables used to estimate the response mechanism (the response probabilities), (b) variables (here called co-variates) that explain the variable of interest, in the usual regression theory sense. This paper, based on Särndal and Swensson (1985 a, b), discusses nonresponse adjusted estimators with and without explicit involvement of co-variates. We conclude that the presence of strong co-variates in an estimator induces several favourable properties. Among other things, estimators making use of co-variates are considerably more resistant to nonresponse bias. We discuss the calculation of standard error and valid confidence intervals for estimators involving co-variates. The structure of the standard error is examined and discussed.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214463
    Description:

    The procedure of subsampling the nonrespondents suggested by Hansen and Hurwitz (1946) is considered. Post-stratification prior to the subsampling is examined. For the mean of a characteristic of interest, ratio estimators suitable for different practical situations are proposed and their merits are examined. Suitable ratio estimators are also suggested for the situations in which the Hard-Core are present.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214446
    Description:

    This paper discusses the influence of the sampling design on the estimation of a linear regression model. Particularly, sampling designs will be discussed which are dependent on the values of the endogenous variable in the population: endogenous (or “informative”) designs. A consistent estimator of the regression coefficients is given. Its variance is the sum of a sampling design component and a disturbance term component. Also, model-free regression is briefly discussed. The model-free regression estimator is the same as the model estimator in the case of an endogenous design.

    Release date: 1986-12-15

  • 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

  • Articles and reports: 12-001-X198600114404
    Description:

    Missing survey data occur because of total nonresponse and item nonresponse. The standard way to attempt to compensate for total nonresponse is by some form of weighting adjustment, whereas item nonresponses are handled by some form of imputation. This paper reviews methods of weighting adjustment and imputation and discusses their properties.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114440
    Description:

    Statistics Canada has undertaken a project to develop a generalized edit and imputation system, the intent of which is to meet the processing requirements of most of its surveys. The various approaches to imputation for item non-response, which have been proposed, will be discussed. Important issues related to the implementation of these proposals into a generalized setting will also be addressed.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114437
    Description:

    In this paper, different types of response/nonresponse and associated measures such as rates are provided and discussed together with their implications on both estimation and administrative procedures. The missing data problems lead to inconsistent terminology related to nonresponse such as completion rates, eligibility rates, contact rates, and refusal rates, many of which can be defined in different ways. In addition, there are item nonresponse rates as well as characteristic response rates. Depending on the uses, the rates may be weighted or unweighted.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114442
    Description:

    For periodic business surveys which are conducted on a monthly, quarterly or annual basis, the data for responding units must be edited and the data for non-responding units must be imputed. This paper reports on methods which can be used for editing and imputing data. The editing is comprised of consistency and statistical edits. The imputation is done for both total non-response and partial non-response.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114438
    Description:

    Using the optimal estimating functions for survey sampling estimation (Godambe and Thompson 1986), we obtain some optimality results for nonresponse situations in survey sampling.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114443
    Description:

    When the technique of adjustment using weighting classes is applied to compensate for the effect of non-response, several questions arise that call for precise and quantified answers: How does the choice of the variables used for definition of the classes affect total root-mean-square error, in particular non-response bias and sampling variance? What rule and what procedure should be followed in choosing the adjustment variables? On the basis of what criterion can the optimal sizes for the weighting classes be established? Finally, when this procedure is applied to compensate for non-response with respect to specific elements of a questionnaire, how can strongly correlated ancillary variables be used effectively when they themselves are affected by non-response? This article is addressed to those professionals working at a practical level who are seeking guidelines.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114444
    Description:

    A new processing system using the nearest neighbour (N-N) imputation method is being implemented for the National Farm Survey (NFS). An empirical study was conducted to determine if the NFS estimates would be affected by using imputation groups based on type of farm. For the specific imputation rule examined, the study showed evidence that the effect might be small.

    Release date: 1986-06-16

  • 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

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Analysis (18)

Analysis (18) (18 of 18 results)

  • Articles and reports: 12-001-X198600214447
    Description:

    The Canadian Health and Disability Survey, administered as a supplement to the Canadian Labour Force Survey in October 1983, collected data on potentially disabled persons by means of a screening questionnaire and a follow-up questionnaire for those screened-in. The data from the screening questionnaire, consisting of a set of activities of daily living, were used to group respondents according to identifiable characteristics. A description of the groups of respondents is provided along with an evaluation of the methods used in their determination. An incompletely ordered severity scale is proposed.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214448
    Description:

    The seasonal adjustment of a time series is not a straightforward procedure particularly when the level of a series nearly doubles in just one year. The 1981-82 recession had a very sudden great impact not only on the structure of the series but on the estimation of the trend- cycle and seasonal components at the end of the series. Serious seasonal adjustment problems can occur. For instance: the selection of the wrong decomposition model may produce underadjustment in the seasonally high months and overadjustment in the seasonally low months. The wrong decomposition model may also signal a false turning point. This article analyses these two aspects of the interplay between a severe recession and seasonal adjustment.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214445
    Description:

    A problem of estimating monthly movements in rents based on data collected every four months is explored. Five alternative composite estimators of the rent index are presented and justified, both from an intuitive as well as theoretical point of view. An empirical study testing and comparing the proposed methods is described and summarized. Recommendations are put forth.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214450
    Description:

    From an annual sample of U.S. corporate tax returns, the U.S. Internal Revenue Service provides estimates of population and subpopulation totals for several hundred financial items. The basic sample design is highly stratified and fairly complex. Starting with the 1981 and 1982 samples, the design was altered to include a double sampling procedure. This was motivated by the need for better allocation of resources, in an environment of shrinking budgets. Items not observed in the subsample are predicted, using a modified hot deck imputation procedure. The present paper describes the design, estimation, and evaluation of the effects of the new procedure.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214451
    Description:

    The Canadian Census of Construction (COC) uses a complex plan for sampling small businesses (those having a gross income of less than $750,000). Stratified samples are drawn from overlapping frames. Two subsamples are selected independently from one of the samples, and more detailed information is collected on the businesses in the subsamples. There are two possible methods of estimating totals for the variables collected in the subsamples. The first approach is to determine weights based on sampling rates. A number of different weights must be used. The second approach is to impute values to the businesses included in the sample but not in the subsamples. This approach creates a complete “rectangular” sample file, and a single weight may then be used to produce estimates for the population. This “large-scale imputation” technique is presently applied for the Census of Construction. The purpose of the study is to compare the figures obtained using various estimation techniques with the estimates produced by means of large-scale imputation.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214449
    Description:

    Nearly all surveys and censuses are subject to two types of nonresponse: unit (total) and item (partial). Several methods of compensating for nonresponse have been developed in an attempt to reduce the bias associated with nonresponse. This paper summarizes the nonresponse adjustment procedures used at the U.S. Census Bureau, focusing on unit nonresponse. Some discussion of current and future research in this area is also included.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214462
    Description:

    In the presence of unit nonresponse, two types of variables can sometimes be observed for units in the “intended” sample s, namely, (a) variables used to estimate the response mechanism (the response probabilities), (b) variables (here called co-variates) that explain the variable of interest, in the usual regression theory sense. This paper, based on Särndal and Swensson (1985 a, b), discusses nonresponse adjusted estimators with and without explicit involvement of co-variates. We conclude that the presence of strong co-variates in an estimator induces several favourable properties. Among other things, estimators making use of co-variates are considerably more resistant to nonresponse bias. We discuss the calculation of standard error and valid confidence intervals for estimators involving co-variates. The structure of the standard error is examined and discussed.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214463
    Description:

    The procedure of subsampling the nonrespondents suggested by Hansen and Hurwitz (1946) is considered. Post-stratification prior to the subsampling is examined. For the mean of a characteristic of interest, ratio estimators suitable for different practical situations are proposed and their merits are examined. Suitable ratio estimators are also suggested for the situations in which the Hard-Core are present.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600214446
    Description:

    This paper discusses the influence of the sampling design on the estimation of a linear regression model. Particularly, sampling designs will be discussed which are dependent on the values of the endogenous variable in the population: endogenous (or “informative”) designs. A consistent estimator of the regression coefficients is given. Its variance is the sum of a sampling design component and a disturbance term component. Also, model-free regression is briefly discussed. The model-free regression estimator is the same as the model estimator in the case of an endogenous design.

    Release date: 1986-12-15

  • 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

  • Articles and reports: 12-001-X198600114404
    Description:

    Missing survey data occur because of total nonresponse and item nonresponse. The standard way to attempt to compensate for total nonresponse is by some form of weighting adjustment, whereas item nonresponses are handled by some form of imputation. This paper reviews methods of weighting adjustment and imputation and discusses their properties.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114440
    Description:

    Statistics Canada has undertaken a project to develop a generalized edit and imputation system, the intent of which is to meet the processing requirements of most of its surveys. The various approaches to imputation for item non-response, which have been proposed, will be discussed. Important issues related to the implementation of these proposals into a generalized setting will also be addressed.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114437
    Description:

    In this paper, different types of response/nonresponse and associated measures such as rates are provided and discussed together with their implications on both estimation and administrative procedures. The missing data problems lead to inconsistent terminology related to nonresponse such as completion rates, eligibility rates, contact rates, and refusal rates, many of which can be defined in different ways. In addition, there are item nonresponse rates as well as characteristic response rates. Depending on the uses, the rates may be weighted or unweighted.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114442
    Description:

    For periodic business surveys which are conducted on a monthly, quarterly or annual basis, the data for responding units must be edited and the data for non-responding units must be imputed. This paper reports on methods which can be used for editing and imputing data. The editing is comprised of consistency and statistical edits. The imputation is done for both total non-response and partial non-response.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114438
    Description:

    Using the optimal estimating functions for survey sampling estimation (Godambe and Thompson 1986), we obtain some optimality results for nonresponse situations in survey sampling.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114443
    Description:

    When the technique of adjustment using weighting classes is applied to compensate for the effect of non-response, several questions arise that call for precise and quantified answers: How does the choice of the variables used for definition of the classes affect total root-mean-square error, in particular non-response bias and sampling variance? What rule and what procedure should be followed in choosing the adjustment variables? On the basis of what criterion can the optimal sizes for the weighting classes be established? Finally, when this procedure is applied to compensate for non-response with respect to specific elements of a questionnaire, how can strongly correlated ancillary variables be used effectively when they themselves are affected by non-response? This article is addressed to those professionals working at a practical level who are seeking guidelines.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114444
    Description:

    A new processing system using the nearest neighbour (N-N) imputation method is being implemented for the National Farm Survey (NFS). An empirical study was conducted to determine if the NFS estimates would be affected by using imputation groups based on type of farm. For the specific imputation rule examined, the study showed evidence that the effect might be small.

    Release date: 1986-06-16

  • 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

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