Journal articles: 'Statistics and methodology'

1

Obuchowski, Nancy A., and Michael L. Lieber. "Statistics and methodology." Skeletal Radiology 37, no. 5 (May 2008): 393–96. http://dx.doi.org/10.1007/s00256-008-0448-1.

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Whitaker, Heather J., and Yonas Ghebremichael-Weldeselassie. "Self-Controlled Case Series Methodology." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 2019): 241–61. http://dx.doi.org/10.1146/annurev-statistics-030718-105108.

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The self-controlled case series method is an epidemiological study design in which individuals act as their own control. The method offers advantages but has several limitations. This article outlines the self-controlled case series method and reviews methodological developments that address some of these limitations.

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RC and W. Paul Vogt. "Dictionary of Statistics and Methodology." Journal of the American Statistical Association 94, no. 446 (June 1999): 657. http://dx.doi.org/10.2307/2670204.

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AA and W. Paul Vogt. "Dictionary of Statistics and Methodology." Journal of the American Statistical Association 89, no. 427 (September 1994): 1150. http://dx.doi.org/10.2307/2290972.

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Luo, Shan, and Ethan Vishniac. "Three-dimensional shape statistics: Methodology." Astrophysical Journal Supplement Series 96 (February 1995): 429. http://dx.doi.org/10.1086/192126.

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Briscoe, Simon, and W. P. Vogt. "Dictionary of Statistics and Methodology." Statistician 43, no. 3 (1994): 473. http://dx.doi.org/10.2307/2348607.

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Giesbrecht, N., Y. Sell, C. Scialfa, L. Sandals, and P. Ehlers. "Essential Topics in Introductory Statistics and Methodology Courses." Teaching of Psychology 24, no. 4 (October 1997): 242–46. http://dx.doi.org/10.1207/s15328023top2404_2.

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The purpose of this research is to assess interdisciplinary agreement about the content of an introductory course in statistics and research methods. We compiled a set of methodological and statistical topics from relevant research, introductory textbooks, and course descriptions. Eighteen professors in the natural sciences, social sciences, and education assessed the importance of these topics for a hypothetical introductory statistics and research methods course. Results indicate agreement across disciplines on the relative importance of 97% of the statistical topics, with 75% of these topics deemed to be important. In contrast, there is agreement on the relative importance of only 48% of the research methods topics. The results are useful in the evaluation of existing statistics courses and texts. In addition, the data provide support for interdisciplinary introductory statistics instruction but also raise questions regarding the apparent discipline-specific nature of instruction in methodology.

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Boersma, Eric, Ron van Domburg, Sanne Hoeks, Isabella Kardys, and Mattie Lenzen. "EuroIntervention - Methodology and Statistics Review Board." EuroIntervention 9, no. 1 (May 2013): 21. http://dx.doi.org/10.4244/eijv9i1a3.

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Degraeuwe, P. L. J., F. H. M. Nieman, A. Yaramis, and K. Bilgin. "Caution About Trial Methodology and Statistics." PEDIATRICS 108, no. 6 (December 1, 2001): 1383–84. http://dx.doi.org/10.1542/peds.108.6.1383-a.

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Stoimenova, Eugenia. "Methodology in robust and nonparametric statistics." Journal of Applied Statistics 40, no. 12 (July 4, 2013): 2773. http://dx.doi.org/10.1080/02664763.2013.816029.

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Degraeuwe, Pieter L. J., and Fred H. M. Nieman. "Caution About Trial Methodology and Statistics." Pediatrics 108, no. 6 (December 1, 2001): 1383–84. http://dx.doi.org/10.1542/peds.108.6.1383b.

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Molenaar, Ivo W. "[The Impact of Sociological Methodology on Statistical Methodology]: Comment: The Fence Between Statistics and Social Research." Statistical Science 7, no. 2 (May 1992): 203–5. http://dx.doi.org/10.1214/ss/1177011358.

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Templ, Matthias, and Valentin Todorov. "The Software Environment R for Official Statistics and Survey Methodology." Austrian Journal of Statistics 45, no. 1 (February 29, 2016): 97–124. http://dx.doi.org/10.17713/ajs.v45i1.100.

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The open-source programming language and software environment R is currently one of the most widely used and popular software tools for statistics and data analysis. This contribution provides an overview of important R packages used in official statistics and survey methodology and discusses the usefulness of R in the daily work of a statistical office. Examples of activities and developments in R related projects in several national and international statistical offices are given. The focus is not only on the internal infrastructure that national and international statistical offices provide for using R but also on some interesting R related projects carried out in those institutes. Two particular packages (laeken and sdcMicro) and one data set (Statistics on Earnings Survey) are used to illustrate the usefulness (and the user-friendliness) of R and to present methods available in R. In addition, the access to international statistical databases like WDI of World Bank and UN COMTRADE with R is illustrated.

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Naddeo, A. "Italian Contributions to the Methodology of Statistics." Biometrics 44, no. 1 (March 1988): 319. http://dx.doi.org/10.2307/2531936.

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JUNG, Won-Gyo, Sang-Sung PARK, and Dong-Sik JANG. "Patent Registration Prediction Methodology Using Multivariate Statistics." IEICE Transactions on Information and Systems E94-D, no. 11 (2011): 2219–26. http://dx.doi.org/10.1587/transinf.e94.d.2219.

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SCHEUNEMAN, JANICE DOWD. "The Female Perspective on Methodology and Statistics." Educational Researcher 15, no. 6 (June 1986): 22–23. http://dx.doi.org/10.3102/0013189x015006022.

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Cheek, P. J., and A. Naddeo. "Italian Contributions to the Methodology of Statistics." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 379. http://dx.doi.org/10.2307/2982789.

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Nusser, Sarah M. "Survey Methodology." Journal of the American Statistical Association 101, no. 475 (September 2006): 1310–11. http://dx.doi.org/10.1198/jasa.2006.s123.

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Radermacher, Walter J. "Guidelines on indicator methodology: A mission impossible?" Statistical Journal of the IAOS 37, no. 1 (March 22, 2021): 205–17. http://dx.doi.org/10.3233/sji-200724.

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Indicators are part of daily life, modern politics and public debate in times of crises and their solutions. The question arises to what extent indicators embody a special form of statistical information, whether a separate indicator-chapter in statistical methodology is needed and what the content of this chapter should be. This paper considers different perspectives of indicators produced by official statistics agencies. Starting from the definition of indicators, it then discusses statistical quality, impact and interaction between producers and users of indicators. To introduce and illustrate the points made in the paper a choice of cases is used in order to derive the essential elements of a methodological framing. The overall objective is to enhance the added value of official statistics indicators as they are communicated and expected to lead to trustworthy evidence for policy making.

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Novikova, Alla, Natalia Myhailyuk, Anatolii Redzyuk, Inna Shum, and Tatiana Yaschenko. "COMPARATIVE ANALYSIS OF METHODOLOGY ROAD TRANSPORT STATISTICS OF EUROSTAT AND UKRAINE." Avtoshliakhovyk Ukrayiny, no. 4 (260) ’ 2019 (December 28, 2019): 2–9. http://dx.doi.org/10.33868/0365-8392-2019-4-260-2-9.

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Methodological approaches to European Union statistical observations of road transport industry are described and comparative analysis of the methodology of statistical observations in Ukraine are performed. The significance of statistical analysis and research of publications on the results of statistical observations for the economy of the country, business environment, scientists and other users is established. The importance database for the National Transport Model is discussed. It is a powerful tool for analyzing, forecasting, feasibility studies of investment projects. The main indicators of statistical reporting for road transport in Ukraine are analyzed. The gaps in the statistical data road transport have been identified. The table provides a clear demonstration of the comparison of Eurostat and Ukraine’s road transport methodology by type of indicators. Differences in methodology with Eurostat in reporting areas were identified: rolling stock; enterprises, economic indicators and employment; road traffic; statistics of passenger road transport; freight road transport; national road freight transport; international road freight transport; cabotage by road. Conclusions and suggestions on converting positions and methodology of national statistics to Eurostat methodology have been provided. It is proposed to create a single electronic platform, accumulating all the various statistics on: carriers, routes, volumes and destinations of transportation, transport work, rolling stock, runs, fuel costs, financial results of activity, investment activity, cross-border traffic, export-import, road accidents, etc. that will be accessible to all potential users of statistics. Keywords: statistics, data base, methodology, reporting forms, sample surveys, road transport.

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Kodila-Tedika, Oasis. "Africa's statistical tragedy: best statistics, best government effectiveness." International Journal of Development Issues 13, no. 2 (July 1, 2014): 171–78. http://dx.doi.org/10.1108/ijdi-12-2013-0090.

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Purpose – The author aims to analyze the effect of statistical capacity on government effectiveness/efficiency using cross-sectional from a sample of 48 African countries for the period 2003-2008. Design/methodology/approach – The estimation technique used is a two-stage least squares instrumental variable methodology and ordinary least square. Findings – The results show that statistical capacity positively affects government effectiveness/efficiency. It follows that countries with higher statistical capacity levels enjoy institutions of better quality than countries with low levels of statistical capacity. Practical implications – As a policy implication, if Africa does not have effective governments, it is partly because it has a very weak statistical capacity. In such an environment, access to information for effective governance is compromised. Originality/value – This paper contributes to existing literature on the determinants of institution by focusing on the distribution of the dependent variable (government effectiveness). The author stressed the importance of information and statistics capabilities.

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22

Taplin, Ross H. "Teaching Statistical Consulting Before Statistical Methodology." Australian New Zealand Journal of Statistics 45, no. 2 (June 2003): 141–52. http://dx.doi.org/10.1111/1467-842x.00270.

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23

Bartholomew, David J. "[The Impact of Sociological Methodology on Statistical Methodology]: Comment." Statistical Science 7, no. 2 (May 1992): 196–98. http://dx.doi.org/10.1214/ss/1177011355.

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Manski, Charles F. "[The Impact of Sociological Methodology on Statistical Methodology]: Comment." Statistical Science 7, no. 2 (May 1992): 201–3. http://dx.doi.org/10.1214/ss/1177011357.

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25

Clogg, Clifford C. "[The Impact of Sociological Methodology on Statistical Methodology]: Rejoinder." Statistical Science 7, no. 2 (May 1992): 205–7. http://dx.doi.org/10.1214/ss/1177011359.

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26

King, Gary. "On Political Methodology." Political Analysis 2 (1990): 1–29. http://dx.doi.org/10.1093/pan/2.1.1.

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“Politimetrics” (Gurr 1972), “polimetrics,” (Alker 1975), “politometrics” (Hilton 1976), “political arithmetic” (Petty [1672] 1971), “quantitative Political Science (QPS),” “governmetrics,” “posopolitics” (Papayanopoulos 1973), “political science statistics” (Rai and Blydenburgh 1973), “political statistics” (Rice 1926). These are some of the names that scholars have used to describe the field we now call “political methodology.”1The history of political methodology has been quite fragmented until recently, as reflected by this patchwork of names. The field has begun to coalesce during the past decade; we are developing persistent organizations, a growing body of scholarly literature, and an emerging consensus about important problems that need to be solved.

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Cowan, Charles D., Robert M. Groves, Paul P. Biemer, Lars E. Lyberg, James T. Massey, William L. Nicholls II, and Joseph Waksberg. "Telephone Survey Methodology." Journal of the American Statistical Association 86, no. 413 (March 1991): 251. http://dx.doi.org/10.2307/2289750.

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28

Wasserman, Stanley, and Clifford C. Clogg. "Sociological Methodology, 1987." Journal of the American Statistical Association 84, no. 405 (March 1989): 347. http://dx.doi.org/10.2307/2289905.

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Firebaugh, Glenn, and Nancy Brandon Tuma. "Sociological Methodology 1986." Journal of the American Statistical Association 83, no. 402 (June 1988): 570. http://dx.doi.org/10.2307/2288892.

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Williams, David, R. M. Groves, P. P. Biemer, L. E. Lyberg, J. T. Massey, W. L. Nicholls II, and J. Waksberg. "Telephone Survey Methodology." Applied Statistics 39, no. 2 (1990): 264. http://dx.doi.org/10.2307/2347770.

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31

Keele, Luke. "The Statistics of Causal Inference: A View from Political Methodology." Political Analysis 23, no. 3 (2015): 313–35. http://dx.doi.org/10.1093/pan/mpv007.

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Many areas of political science focus on causal questions. Evidence from statistical analyses is often used to make the case for causal relationships. While statistical analyses can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on specific statistical methods, such as matching, I focus more on the assumptions needed to give statistical estimates a causal interpretation. Such assumptions are often referred to as identification assumptions, and these assumptions are critical to any statistical analysis about causal effects. I outline a wide range of identification assumptions and highlight the design-based approach to causal inference. I conclude with an overview of statistical methods that are frequently used for causal inference.

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32

Poulton, A. "Attention-Deficit/Hyperactivity Disorder, Stimulants, Statistics, and Methodology." Journal of Child and Adolescent Psychopharmacology 16, no. 4 (August 2006): 507–8. http://dx.doi.org/10.1089/cap.2006.16.507.

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33

Shore, Haim. "Response modeling methodology." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 4 (March 25, 2011): 357–72. http://dx.doi.org/10.1002/wics.151.

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34

Herzing, Jessica M. E. "Web Survey Methodology." Journal of Official Statistics 32, no. 4 (December 1, 2016): 1011–14. http://dx.doi.org/10.1515/jos-2016-0053.

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35

Zirmansyah, Zirmansyah. "Kualitas Skripsi Mahasiswa Universitas Al Azhar Indonesia: Pengaruh Hasil Belajar Metodologi Penelitian dan Statistik terhadap Kualitas Skripsi." JURNAL Al-AZHAR INDONESIA SERI HUMANIORA 1, no. 1 (April 4, 2011): 19. http://dx.doi.org/10.36722/sh.v1i1.20.

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The objective of the research is to study the relationship between learning outcome on statistic, learning outcome on research methodology and thesis quality. The research was carried out at the student Al Azhar University, with 53 samples of thesis which were selected randomly. The research concludes there is positive correlation between: (1) learning outcome on statistics and thesis quality; (2) knowledge on research methodology and thesis quality; (3) furthermore, there is a positive correlation between learning outcome on statistcs, learning outcome on research methodology, with thesis quality. Therefore thesis quality can be increased by improving learning outcome on statistic, and learning outcome on research metodhology.

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36

Candes, Emmanuel J., and Terrence L. Fine. "Feedforward Neural Network Methodology." Journal of the American Statistical Association 95, no. 450 (June 2000): 682. http://dx.doi.org/10.2307/2669423.

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37

MW and Dale J. Poirier. "The Methodology of Econometrics." Journal of the American Statistical Association 90, no. 432 (December 1995): 1493. http://dx.doi.org/10.2307/2291558.

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38

Pepe, Margaret Sullivan. "Receiver Operating Characteristic Methodology." Journal of the American Statistical Association 95, no. 449 (March 2000): 308–11. http://dx.doi.org/10.1080/01621459.2000.10473930.

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Durr, Jean-Michel, and Nicole Roth. "Towards certification of new statistics of public interest in France." Hungarian Statistical Review 3, no. 2 (2020): 3–11. http://dx.doi.org/10.35618/hsr2020.02.en003.

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This paper presents the analysis and proposal to qualify new statistics of public interest in France. It describes the present situation regarding the qualification of statistics produced independent of the National Statistical System and the challenges related to their emergence, as these statistics may complement and sometimes challenge official statistics. The authors propose a three-tier mechanism; the first level is limited to self-documentation of a statistic based on a predefined template, the second level is the approval of such documentation, and the third level involves the approval of the documentation including the methodology and quality of output.

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40

Tzanis, Chris G., Anastasios Alimissis, and Ioannis Koutsogiannis. "Application of a Machine Learning Methodology for Data Implementation." Environmental Sciences Proceedings 4, no. 1 (November 14, 2020): 11. http://dx.doi.org/10.3390/ecas2020-08156.

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An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air quality monitoring networks that operate in urban areas provide data on the most important pollutants, which via environmental statistics can be used for the development of continuous surfaces of pollutants’ concentrations. Generating ambient air quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air quality distributions. This study used point monitoring data from the network of stations that operates in Athens. A machine learning scheme was applied as a method to spatially estimate pollutants’ concentrations and the results could be effectively used to implement missing values and provide representative data for statistical analyses purposes.

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41

Ware, James H., and Donald A. Berry. "Statistical Methodology in the Pharmaceutical Sciences." Journal of the American Statistical Association 86, no. 413 (March 1991): 250. http://dx.doi.org/10.2307/2289748.

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42

Jogesh Babu, G. "Kesar Singh’s contributions to statistical methodology." Statistical Methodology 20 (September 2014): 2–10. http://dx.doi.org/10.1016/j.stamet.2013.12.001.

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M. P., Iwundu. "Useful Numerical Statistics of Some Response Surface Methodology Designs." Journal of Mathematics Research 8, no. 4 (July 25, 2016): 40. http://dx.doi.org/10.5539/jmr.v8n4p40.

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<p>Useful numerical evaluations associated with three categories of Response Surface Methodology designs are presented with respect to five commonly encountered alphabetic optimality criteria. The first-order Plackett-Burman designs and the Factorial designs are examined for the main effects models and the complete first-order models respectively. The second-order Central Composite Designs are examined for second-order models. The A-, D-, E-, G- and T-optimality criteria are employed as commonly encountered optimality criteria summarizing how good the experimental designs are. Relationships among the optimality criteria are pointed out with regards to the designs and the models. Generally the designs do not show uniform preferences in terms of the considered optimality criteria. However, one interesting finding is that central composite designs defined on cubes and hypercubes with unit axial distances are uniformly preferred in terms of E-optimality and G-optimality criteria.</p>

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44

Sumathi, P. "Computer Aided Interactive Process of Teaching Statistics Methodology - I." IEIT Journal of Adaptive and Dynamic Computing; 2011, no. 2 (2011): 1. http://dx.doi.org/10.5813/www.ieit-web.org/ijadc/2011.2.1.

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Vani, M. P. "Computer Aided Interactive Process of Teaching Statistics Methodology - II." IEIT Journal of Adaptive and Dynamic Computing; 2011, no. 3 (2011): 18. http://dx.doi.org/10.5813/www.ieit-web.org/ijadc/2011.3.4.

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46

Zhao, Xiaohui, Keith Dowd, and Cynthia A. Searcy. "Assessing Statistics and Research Methodology in the MCAT Exam." CHANCE 25, no. 3 (September 10, 2012): 11–17. http://dx.doi.org/10.1080/09332480.2012.726556.

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47

Jose, Paul E. "OBSERVATIONS ABOUT HOW WE LEARN ABOUT METHODOLOGY AND STATISTICS." Monographs of the Society for Research in Child Development 82, no. 2 (May 5, 2017): 140–51. http://dx.doi.org/10.1111/mono.12303.

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48

Bécue-Bertaut, Mónica, Belchin Kostov, Annie Morin, and Guilhem Naro. "Rhetorical Strategy in Forensic Speeches: Multidimensional Statistics-Based Methodology." Journal of Classification 31, no. 1 (March 29, 2014): 85–106. http://dx.doi.org/10.1007/s00357-014-9148-9.

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49

Porter, Theodore M., and A. Naddeo. "Italian Contributions to the Methodology of Statistic." Journal of the American Statistical Association 83, no. 403 (September 1988): 908. http://dx.doi.org/10.2307/2289331.

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50

Du, Chao, and S. C. Kou. "Statistical Methodology in Single-Molecule Experiments." Statistical Science 35, no. 1 (February 2020): 75–91. http://dx.doi.org/10.1214/19-sts752.

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Journal articles on the topic 'Statistics and methodology'

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