Balanced k-nearest neighbour imputation

Random imputation is an interesting class of imputation methods to handle item nonresponse because it tends to preserve the distribution of the imputed variable. However, such methods amplify the total variance of the estimators because values are imputed at random. This increase in variance is called imputation variance. In this paper, we propose a new random hot-deck imputation method that is based on the k-nearest neighbour methodology. It replaces the missing value of a unit with the observed value of a similar unit. Calibration and balanced sampling are applied to minimize the imputation variance. Moreover, our proposed method provides triple protection against nonresponse bias. This means that if at least one out of three specified models holds, then the resulting total estimator is unbiased. Finally, our approach allows the user to perform consistency edits and to impute simultaneously.     

Item nonresponse on income questions in panel surveys: Incidence, imputation and the impact on inequality and mobility

Summary: This paper deals with item nonresponse on income questions in panel surveys and with longitudinal and cross–sectional imputation strategies to cope with this phenomenon. Using data from the German SOEP, we compare income inequality and mobility indicators based only on truly observed information to those derived from observed and imputed observations. First, we find a positive correlation between inequality and imputation. Secondly, income mobility appears to be significantly understated using observed information only. Finally, longitudinal analyses provide evidence for a positive inter–temporal correlation between item nonresponse and any kind of subsequent nonresponse.* We are grateful to two anonymous referees and to Jan Goebel for very helpful comments and suggestions on an earlier draft of this paper. The paper also benefited from discussions with seminar participants at the Workshop on Item Nonresponse and Data Quality in Large Social Surveys, Basel/CH, October 9–11, 2003.     

Nonresponse in a Periodic Sample Survey

A parametric model is developed for analyzing the updated historical (ratio of identicals) imputation strategy in a periodic survey. A test is then developed for comparing alternative formulations of the historical values. The benefits of exponentially smoothing in this context are demonstrated using monthly gasoline sales data.     

A calibrated imputation method for secondary data analysis of survey data

In practical survey sampling, missing data are unavoidable due to nonresponse, rejected observations by editing, disclosure control, or outlier suppression. We propose a calibrated imputation approach so that valid point and variance estimates of the population (or domain) totals can be computed by the secondary users using simple complete‐sample formulae. This is especially helpful for variance estimation, which generally require additional information and tools that are unavailable to the secondary users. Our approach is natural for continuous variables, where the estimation may be either based on reweighting or imputation, including possibly their outlier‐robust extensions. We also propose a multivariate procedure to accommodate the estimation of the covariance matrix between estimated population totals, which facilitates variance estimation of the ratios or differences among the estimated totals. We illustrate the proposed approach using simulation data in supplementary materials that are available online.     

Some problems of optimal allocation in sample surveys involving inequality constraints

Four optimal sample allocation problems, involving inequality constraints of a special type, are investigated, Solutions are obtained by employing an extension of a result due to Thompson (1962). Applications are given.     

Are we keeping the people who used to stay? Changes in correlates of panel survey attrition over time

As survey response rates decline, correlates of survey participation may also be changing. Panel studies provide an opportunity to study a rich set of correlates of panel attrition over time. We look at changes in attrition rates in the American National Election Studies from 1964 to 2004, a repeated panel survey with a two-wave pre-post election design implemented over multiple decades. We examine changes in attrition rates by three groups of variables: sociodemographic and ecological characteristics of the respondent and household, party affiliation and political and social attitudes recorded at the first interview, and paradata about the first wave interview. We find relatively little overall change in the pre-post election panel attrition rates, but important changes in demographic correlates of panel attrition over time. We also examine contact and cooperation rates from 1988 to 2004.     

Representativeness in panel surveys

ABSTRACT

A panel is a set of units recruited and used in successive surveys. When the sample unit is the household, so-called R-indicators together with the comparison of distributions of certain variables to those known in the total population help to measure the representativeness of the panel. The method is applied to Understanding Society, a U.K. household longitudinal study. At each wave, under- and over-represented groups of individuals are identified. This allows the implementation of better survey designs and procedures to reduce the bias of nonresponse.     

Effects of screening questionnaires on response in a two-phase postal survey

For surveys targeting specific population groups, the two-phase postal approach (screener followed by a topical survey sent to eligible households) has been demonstrated to be more effective at identifying population domains of interest than random digit dial telephone methods considering cost, coverage, and response. An important question is how best to motivate screener response from eligible households. In 2011, we conducted a large-scale field test to empirically test a number of methods for motivating response. We fielded screening surveys that varied content-influencing relevance, and also switched screener questionnaires for following up nonrespondents to the initial postal survey – an approach we have labeled responsive tailoring. In another experiment, we tested the effect of asking for first names in the screener questionnaire. In this article, we describe the effects of these experimental treatments on response to both the screener and the topical survey.     

Missing data methods in official statistics in the United Kingdom: Some recent developments

In recent years an increase in nonresponse rates in major government and social surveys has been observed. It is thought that decreasing response rates and changes in nonresponse bias may affect, potentially severely, the quality of survey data. This paper discusses the problem of unit and item nonresponse in government surveys from an applied perspective and highlights some newer developments in this field with a focus on official statistics in the United Kingdom (UK). The main focus of the paper is on post-survey adjustment methods, in particular adjustment for item nonresponse. The use of various imputation and weighting methods is discussed in an example. The application also illustrates the close relationship between missing data and measurement error. JEL classification C42, C81     

Imputation of Household Survey Data Using Linear Mixed Models

Mixed models are regularly used in the analysis of clustered data, but are only recently being used for imputation of missing data. In household surveys where multiple people are selected from each household, imputation of missing values should preserve the structure pertaining to people within households and should not artificially change the apparent intracluster correlation (ICC). This paper focuses on the use of multilevel models for imputation of missing data in household surveys. In particular, the performance of a best linear unbiased predictor for both stochastic and deterministic imputation using a linear mixed model is compared to imputation based on a single level linear model, both with and without information about household respondents. In this paper an evaluation is carried out in the context of imputing hourly wage rate in the Household, Income and Labour Dynamics of Australia Survey. Nonresponse is generated under various assumptions about the missingness mechanism for persons and households, and with low, moderate and high intra‐household correlation to assess the benefits of the multilevel imputation model under different conditions. The mixed model and single level model with information about the household respondent lead to clear improvements when the ICC is moderate or high, and when there is informative missingness.