Roderick J A Little

Dados Biográficos

ID331607
NOMERoderick J A Little
PRENOMESRoderick J A
SOBRENOMELittle
ASSINATURALITTLE R J A
AFILIAÇÕESUniversity of Michigan
VERIFICADONão
TOTAL DE OBRAS10
TOTAL DE CITAÇÕES90
TOTAL COMO AUTOR10
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO1979
ANO MAIS RECENTE DE PUBLICAÇÃO2010
ÍNDICE H3

  • A Review of Hot Deck Imputation for Survey Non‐response

    Open AccessRebecca R Andridge, Roderick J A Little et al.ARTICLEInternational Statistical Review2010

    Hot deck imputation is a method for handling missing data in which each missing value is replaced with an observed response from a “similar” unit. Despite being used extensively in practice, the theory is not as well developed as that of other imputation methods. We have found that no consensus exists as to the best way to apply the hot deck and obtain inferences from the completed data set. Here we review different forms of the hot deck and exis…

  • Statistical analysis with missing data

    Roderick J A LittleBOOKStatistical analysis with missing…2002Citada por: 6

    Incorporating a large body of new work in the field, this second edition includes the latest applications of modern missing-data methods to real data. The authors also examine the theoretical and technical extensions that take advantage of recent computational advances.

  • Modeling the Drop-Out Mechanism in Repeated-Measures Studies

    Roderick J A Little, Roderick J LittleARTICLEJournal of the American…1995

    Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with unequal numbers of measures for each subject. Modern software programs for handling unbalanced longitudinal data improve on methods that discard the incomplete cases by including all the data, but also yield biased inferences under plausible models for the drop-out process. This article discusses methods that simultaneously model the data and the drop-out p…

  • Projecting From Advance Data Using Propensity Modeling

    John L Czajka, Sharon M Hirabayashi et al.ARTICLEJournal of Business and Economic…1992

    This article proposes and evaluates two new methods of reweighting preliminary data to obtain estimates more closely approximating those derived from the final data set. In our motivating example, the preliminary data are an early sample of tax returns, and the final data set is the sample after all tax returns have been processed. The new methods estimate a predicted propensity for late filing for each return in the advance sample and then posts…

  • The Analysis of Social Science Data with Missing Values

    Open AccessRoderick J A Little, Roderick J Little et al.ARTICLESociological Methods & Research1989Citada por: 79Referências: 18

    Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable. Two more principled approaches, namely maximum likelihood under a model for the data and missing-data mechanism and multiple imputation, are applied to the biva…

  • A Test of Missing Completely at Random for Multivariate Data with Missing Values

    Roderick J A Little, Roderick J LittleARTICLEJournal of the American…1988

    A common concern when faced with multivariate data with missing values is whether the missing data are missing completely at random (MCAR); that is, whether missingness depends on the variables in the data set. One way of assessing this is to compare the means of recorded values of each variable between groups defined by whether other variables in the data set are missing or not. Although informative, this procedure yields potentially many correl…

  • [Missing-Data Adjustments in Large Surveys]

    Roderick J A Little, Roderick J LittleARTICLEJournal of Business and Economic…1988

  • Missing-Data Adjustments in Large Surveys

    Roderick J A Little, Roderick J LittleARTICLEJournal of Business and Economic…1988

    Useful properties of a general-purpose imputation method for numerical data are suggested and discussed in the context of several large government surveys. Imputation based on predictive mean matching is proposed as a useful extension of methods in existing practice, and versions of the method are presented for unit nonresponse and item nonresponse with a general pattern of missingness. Extensions of the method to provide multiple imputations are…

  • Survey Nonresponse Adjustments for Estimates of Means

    Roderick J A Little, Roderick J LittleARTICLEInternational Statistical Review1986

    Theoretical properties of nonresponse adjustments based on adjustment cells are studied, for estimates of means for the whole population and in subclasses that cut across adjustment cells. Three forms of adjustment are considered: weighting by the inverse response rate within cells, post-stratification on known population cell counts, and mean imputation within adjustment cells. Two dimensions of covariate information x are distinguished as parti…

  • The General Linear Model and Direct Standardization

    Open AccessRoderick J A Little, Roderick J Little et al.ARTICLESociological Methods & Research1979Citada por: 5Referências: 6

    A formal comparison is made between direct standardization and three cross- classified data structures: tables of means which are linear additive; tables of means which are log-linear additive; and tables of frequencies which are log-linear addi tive and can be converted to tables of proportions which are logit-linear additive. Standardization is an appropriate method of summarizing the data if the differ ences between standardized means and so o…