A general approach for estimating scale score reliability for panel survey data.

Scale score measures are ubiquitous in the psychological literature and can be used as both dependent and independent variables in data analysis. Poor reliability of scale score measures leads to inflated standard errors and/or biased estimates, particularly in multivariate analysis. To assess data quality, reliability estimation is usually an integral step in the analysis of scale score data. Cronbach’s α is a widely used indicator of reliability but, due to its rather strong assumptions, can be a poor estimator (Cronbach, 1951). For longitudinal data, an alternative approach is the simplex method; however, it too requires assumptions that may not hold in practice. One effective approach is an alternative estimator of reliability that relaxes the assumptions of both Cronbach’s α and the simplex estimator and, thus, generalizes both estimators. Using data from a large-scale panel survey, the benefits of the statistical properties of this estimator are investigated and its use is illustrated and compared with the more traditional estimators of reliability

METHODOLOGY FOR OPTIMAL DUAL FRPME SWPLE DESIGN

This series contains research reports, written by or in cooperation with staff members of the Statistical Research Division, whose content may be of interest to the general statistical research community. The views re-flected in these reports are not necessarily those of the Census Bureau nor do they necessarily represent Census Bureau statistical policy or prac-tice. Inquiries may be addressed to the author(s) or the SRD Report Serie

Latent Class Analysis of Survey Error

This book concerns the error in data collected using sample surveys, the nature and magnitudes of the errors, their effects on survey estimates, how to model and estimate the errors using a variety of modeling methods, and, finally, how to interpret the estimates and make use of the results in reducing the error for future surveys. The book focuses on models that are appropriate for categorical data, although there are references to the differences and special problems that arise in the analysis and modeling of error for continuous data. Though the primary modeling method that is described i

An Application of Bootstrapping for Determining a Decision Rule for Site Location

This article provides a general methodology for determining and evaluating a decision rule for hotel site location. Given (a) an indicator of hotel success, (b) an ideal decision rule based on this indicator if it were known without error, and (c) a model for predicting the value of the success indicator at a proposed site, we propose a procedure for finding the optimal model-based decision rule for any specified optimality criterion and for evaluating the worth of the rule. The methodology is based on the statistical technique called bootstrapping. This method reduces the bias of conventional methods of estimation that have been applied in the context of site-location modeling. The methodology is illustrated using data from a large hotel chain in the United States and evaluated using an independent evaluation sample

Using reinterview and reconciliation methods to design and evaluate survey questions

"Conducting reinterviews is an effective method to estimate and reduce response errors in interview surveys. As part of the School Health Policies and Programs Study 2000 (SHPPS), RTI used reinterview methods to assist in designing and evaluating survey questions. Reinterviews were conducted in the field test with selected respondents to identify discrepancies between the original interviews and reinterviews. Reconciliation interviews were then conducted to determine the reasons for the discrepancies in terms of comprehension, recall, encoding, response options, or other problems. In this paper, the authors describe the design of the reinterview and reconciliation study and discuss the implications of using these methods for questionnaire design and evaluation, specifically in comparison to cognitive interviewing." (author's abstract

Estimating underreporting of consumer expenditures using Markov latent class analysis

"This paper examines reporting in specific consumer item categories (or commodities) and estimates expenditure underreporting due to survey respondents who erroneously report no expenditure in a category. The author's approach for estimating underreporting errors is a two-step process. In the first step, a Markov latent class analysis is performed to estimate the proportion of consumers in various subpopulations who fail to report their actual expenditure in a particular commodity. Once this proportion is estimated, the dollar value of the missing expenditure is estimated using the mean expenditure of those in that subpopulation that did report an expenditure. Finally, the estimates are evaluated and discussed in light of external data on expenditure underreporting." (author's abstract