Measuring Representativeness of Short-Term Business Statistics

Ouwehand, Pim; Schouten, Barry

doi:10.2478/jos-2014-0041

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Table 10 presents the findings for R 2 ‐indicator calculated for PANEL data. The value of R 2 indicator is quite high (over 75%) when compared with studies that use similar data and variables (Ouwehand & Schouten, 2014). So is possible to be fairly confident that the level of representativeness is appropriate in a global context.…”

Section: Quality Of the Longitudinal Databasesmentioning

confidence: 70%

Longitudinal business data construction and quality: Two different approaches

Biffignandi

Zeli

2020

Statistica Neerlandica

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Reducing the response burden and widening available statistical information necessitate new approaches in the National Statistical Institutes production process. Our article focuses on longitudinal data needs. Two approaches for building business longitudinal data in a context of cross‐section surveys and administrative sources information are considered. The article describes construction approaches and evaluates the quality of two data bases obtained through multisources integration. The computed databases aim to represent the target population of Italian firms with 20 persons employed and over. The similarity of the distribution of the main economic variables between the target population and the computed databases is considered a basic criterion in evaluating the quality of the created databases. To this end, rank correlation, and the Fligner–Policello test are applied. In addition, representativeness R indicators are computed. No differences are found between distributions.

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Section: Quality Of the Longitudinal Databasesmentioning

confidence: 70%

Longitudinal business data construction and quality: Two different approaches

Biffignandi

Zeli

2020

Statistica Neerlandica

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“…A complicating aspect is that the initially observed data may be from a selective part of the population (see, e.g. Ouwehand and Schouten, , for assessing the representativeness of data). A further issue is that corrections on originally reported data may become available after a long time.…”

Section: General Issues When Combining Datamentioning

confidence: 99%

Multi‐source Statistics: Basic Situations and Methods

Waal

Delden

Scholtus

2019

Int Statistical Rev

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Summary Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems.

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“…These measures were designed as process metrics to be monitored during data collection, similar to other process metrics such as R-indicators: see Ouwehand and Schouten (2014) and Schouten et al (2009), among others. The imbalance and distances measures (IB x and D x ) are approximate chi-square statistics with one degree of freedom and can therefore be tested for significant difference from zero using a large-sample approximation (Johnson and Wichern 1988, 190-191) with a few strong caveats, that is, the variance estimator is very conservative and does not incorporate the finite population sample design features.…”

Section: Indicators Involving the Response Indicator: Type (1) And Tymentioning

confidence: 99%

Investigating Adaptive Nonresponse Follow-up Strategies for Small Businesses through Embedded Experiments

Thompson

Kaputa

2017

Journal of Official Statistics

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The U.S. Census Bureau is investigating adaptive Nonresponse Follow-Up (NRFU) strategies for single unit businesses in the 2017 Economic Census. These collection protocols require a suite of viable alternative procedures that can be implemented. With business surveys, the majority of cognitive research and nonresponse follow-up procedures focus on collection methods that obtain valid response data from the larger businesses, and there is relatively little quantitative or qualitative research for small businesses. Moreover, the contact methods for small businesses are often constrained by budget limitations. Business programs at the U.S. Census Bureau rely on mailed reminder letters and supplemental promotional materials, with options for certified and bulk mailings. To explore the benefits and disadvantages of the proposed alternative nonresponse follow-up procedures for small businesses, we conducted a field experiment embedded in the 2014 Annual Survey of Manufactures, an annual program that has similar data collection procedures and sampling units as the Economic Census. This article describes the study and presents the results, then discusses how the recommended nonresponse follow-up procedures are implemented in an adaptive collection design test presently being conducted in the 2015 Annual Survey of Manufactures.

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Measuring Representativeness of Short-Term Business Statistics

Cited by 7 publications

References 7 publications

Longitudinal business data construction and quality: Two different approaches

Longitudinal business data construction and quality: Two different approaches

Multi‐source Statistics: Basic Situations and Methods

Investigating Adaptive Nonresponse Follow-up Strategies for Small Businesses through Embedded Experiments

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