Volume 36 (2020): Issue 4 (December 2020)

Most countries use either the Jevons or Carli index for the calculation of their Consumer Price Index (CPI) at the lowest (elementary) level of aggregation. The choice of the elementary formula for inflation measurement does matter and the effect of the change of the index formula was estimated by the Bureau of Labor Statistics (2001). It has been shown in the literature that the difference between the Carli index and the Jevons index is bounded from below by the variance of the price relatives. In this article, we extend this result, comparing expected values and variances of these sample indices under the assumption that prices are described by a geometric Brownian motion (GBM). We provide formulas for their biases, variances and mean-squared errors.

Measuring the service flow and the stock value of condominium apartments in Canada and decomposing these values into constant quality price and quantity components is important for many purposes. In addition, the System of National Accounts requires that these service flows and stock values for condos be decomposed into constant quality land and structure components. In Canada and most other countries, such a land and structure decomposition of condominium apartment sale prices does not currently exist. In this article, we provide such a decomposition of condominium apartment sales in Ottawa for the period 1996–2009. Specific attention is paid to the roles of communal land and structure space on condominium apartment unit selling prices. Key findings include methods to allocate land and building space to a single condominium unit, identifying the characteristics that best explain condominium prices, and developing an average depreciation rate for condos for the 14-year time period.

Record linkage addresses the problem of identifying pairs of records coming from different sources and referred to the same unit of interest. Fellegi and Sunter propose an optimal statistical test in order to assign the match status to the candidate pairs, in which the needed parameters are obtained through EM algorithm directly applied to the set of candidate pairs, without recourse to training data. However, this procedure has a quadratic complexity as the two lists to be matched grow. In addition, a large bias of EM-estimated parameters is also produced in this case, so that the problem is tackled by reducing the set of candidate pairs through filtering methods such as blocking. Unfortunately, the probability that excluded pairs would be actually true-matches cannot be assessed through such methods.

The present work proposes an efficient approach in which the comparison of records between lists are minimised while the EM estimates are modified by modelling tables with structural zeros in order to obtain unbiased estimates of the parameters. Improvement achieved by the suggested method is shown by means of simulations and an application based on real data.

Longitudinal or panel surveys are effective tools for measuring individual level changes in the outcome variables and their correlates. One drawback of these studies is dropout or nonresponse, potentially leading to biased results. One of the main reasons for dropout is the burden of repeatedly responding to long questionnaires. Advancements in survey administration methodology and multiple imputation software now make it possible for planned missing data designs to be implemented for improving the data quality through a reduction in survey length. Many papers have discussed implementing a planned missing data study using a split questionnaire design in the cross-sectional setting, but development of these designs in a longitudinal study has been limited. Using simulations and data from the Health and Retirement Study (HRS), we compare the performance of several methods for administering a split questionnaire design in the longitudinal setting. The results suggest that the optimal design depends on the data structure and estimand of interest. These factors must be taken into account when designing a longitudinal study with planned missing data.

In double barreled questions (DBQs) respondents provide one answer to two questions. Assumptions how respondents treat DBQs and how DBQs impact measurement quality are tested in two randomized experiments. DBQs are compared with revisions in which one stimulus was retained while the other stimulus was skipped. The observed means and parameters when modeling latent variables differed among the versions. Metric and scalar measurement invariance was not given among the versions, and at least one single stimulus version was found to be associated with a higher validity. Response latencies did not differ among versions or respondents needed less time to respond to DBQs. The author concludes that respondents may understand the stimuli in a DBQ differently, and access one of them while disregarding the other, which can have an adverse effect on validity.

Like other surveys, time use surveys are facing declining response rates. At the same time paper-and-pencil surveys are increasingly replaced by online surveys. Both the declining response rates and the shift to online research are expected to have an impact on the representativeness of survey data questioning whether they are still the most suitable instrument to obtain a reliable view on the organization of daily life. This contribution examines the representativeness of a self-administered online time use survey using Belgian data collected in 2013 and 2014. The design of the study was deliberately chosen to test the automated processes that replace interviewer support and its cost-efficiency. We use weighting coefficients, a life table and discrete-time survival analyses to better understand the timing and selectivity of dropout, with a focus on the effects of individual time use patterns and the survey design. The results show that there are three major hurdles that cause large groups of respondents to drop out. This dropout is selective, and this selectivity differs according to the dropout moment. The contribution aims to provide a better insight in dropout during the fieldwork and tries to contribute to the further improvement of survey methodology of online time use surveys.

Responsive survey designs rely upon incoming data from the field data collection to optimize cost and quality tradeoffs. In order to make these decisions in real-time, survey managers rely upon monitoring tools that generate proxy indicators for cost and quality. There is a developing literature on proxy indicators for the risk of nonresponse bias. However, there is very little research on proxy indicators for costs and almost none aimed at predicting costs under alternative design strategies. Predictions of survey costs and proxy error indicators can be used to optimize survey designs in real time. Using data from the National Survey of Family Growth, we evaluate alternative modeling strategies aimed at predicting survey costs (specifically, interviewer hours). The models include multilevel regression (with random interviewer effects) and Bayesian Additive Regression Trees (BART).