Paul Biemer, Dennis Trewin, Heather Bergdahl and Lilli Japec
This article describes a general framework for improving the quality of statistical programs in organizations that provide a continual flow of statistical products to users and stakeholders. The work stems from a 2011 mandate to Statistics Sweden issued by the Swedish Ministry of Finance to develop a system of quality indicators for tracking developments and changes in product quality and for achieving continual improvements in survey quality across a diverse set of key statistical products. We describe this system, apply it to a number of products at Statistics Sweden, and summarize key results and lessons learned. The implications of this work for monitoring and evaluating product quality in other statistical organizations are also discussed.
Vanessa Torres van Grinsven, Irena Bolko and Mojca Bavdaž
Increasing reluctance of businesses to participate in surveys often leads to declining or low response rates, poor data quality and burden complaints, and suggests that a driving force, that is, the motivation for participation and accurate and timely response, is insufficient or lacking. Inspiration for ways to remedy this situation has already been sought in the psychological theory of self-determination; previous research has favored enhancement of intrinsic motivation compared to extrinsic motivation. Traditionally however, enhancing extrinsic motivation has been pervasive in business surveys. We therefore review this theory in the context of business surveys using empirical data from the Netherlands and Slovenia, and suggest that extrinsic motivation calls for at least as much attention as intrinsic motivation, that other sources of motivation may be relevant besides those stemming from the three fundamental psychological needs (competence, autonomy and relatedness), and that other approaches may have the potential to better explain some aspects of motivation in business surveys (e.g., implicit motives). We conclude with suggestions that survey organizations can consider when attempting to improve business survey response behavior.
Hyukjun Gweon, Matthias Schonlau, Lars Kaczmirek, Michael Blohm and Stefan Steiner
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Monika Jakubus, Mirosław Krzyśko, Waldemar Wołyński and Małgorzata Graczyk
Recycling of crop residues is essential to sustain soil fertility and crop production. Despite the positive effect of straw incorporation, the slow decomposition of that organic substance is a serious issue. The aim of the study was to assess the influence of winter wheat straws with different degrees of stem solidness on the rate of decomposition and soil properties. An incubation experiment lasting 425 days was carried out in controlled conditions. To perform analyses, soil samples were collected after 7, 14, 21, 28, 35, 49, 63, 77, 91, 119, 147, 175, 203, 231, 259, 313, 341, 369, 397 and 425 days of incubation. The addition of two types of winter wheat straw with different degree of stem solidness into the sandy soil differentiated the experimental treatments. The results demonstrate that straw mineralization was a relatively slow process and did not depend on the degree of filling of the stem by pith. Multivariate functional principal component analysis (MFPC) gave proof of significant variation between the control soil and the soil incubated with the straws. The first functional principal component describes 48.53% and the second 18.55%, of the variability of soil properties. Organic carbon, mineral nitrogen and sum of bases impact on the first functional principal component, whereas, magnesium, sum of bases and total nitrogen impact on the second functional principal component.
Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitance to adopt this approach may stem from an unfamiliarity with the computational tools necessary for computing Bayes factors. Previous work has shown that closed-form approximations of Bayes factors are relatively easy to obtain for between-groups methods, such as an analysis of variance or t-test. In this paper, I extend this approximation to develop a formula for the Bayes factor that directly uses information that is typically reported for ANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples of its use, I report the results of simulations which show that even with minimal input, this approximate Bayes factor produces similar results to existing software solutions.
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Determination of optimum plot size has been regarded as an important and useful area of study for agriculturists and statisticians since the first remarkable contribution on this problem came to light in a paper by Smith (1938). As we explore the scientific literature relating to this problem, we may note a number of contributions, including those of Modjeska and Rawlings (1983), Webster and Burgess (1984), Sethi (1985), Zhang et al. (1990, 1994), Bhatti et al.(1991), Fagroud and Meirvenne (2002), etc. In Pal et al. (2007), a general method was presented by means of which the optimum plot size can be determined through a systematic analytical procedure. The importance of the procedure stems from the fact that even with Fisherian blocking, the correlation among the residuals is not eliminated (as such the residuals remain correlated). The method is based on an application of an empirical variogram constructed on real-life data sets (obtained from uniformity trials) wherein the data are serially correlated. This paper presents a deep and extensive investigation (involving theoretical exploration of the effect of different plot sizes and shapes in discovering the point – actually the minimum radius of curvature of the variogram at that point – beyond which the theoretical variogram assumes stationary values with further increase in lags) in the case of the most commonly employed model (incorporating a correlation structure) assumed to represent real-life data situations (uniformity trial or designed experiments, RBD/LSD).
Bogna Zawieja, Ewa Bakinowska, Andrzej Bichoński and Wiesław Pilarczyk
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Mila A.L. Carriquiry A.L. Yang X.B. (2004): Modeling the prevalence of Sclerotinia stem rot of soybeans in the North Central region of the United States. Phytopathology. 94: 102-110.
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Statkevičiūtė G., Leistrumaitė A. (2010): Modern varieties of spring barley as a genetic