Statistical software

Background Bullying has public health importance. It has been reported that both the victims and perpetrators of bullying are more likely to have suicidal ideation and other suicidal behaviours. Moreover, bullying can be a precursor for school violence and can contribute to poor academic performance. The purpose of the study was to raise awareness on the subject in China. We, therefore conducted an analysis of secondary data to determine the prevalence and correlates of having been bullied among in-school adolescents. Methods The data was taken from the Beijing Global School-Based Health Survey conducted in 2003. A weighted analysis to reduce bias due to differing patterns of non-response was conducted using statistical software (SPSS version 14.0). We conducted a backward logistic regression analysis to determine independent predictors for being bullied. Results Out of a total of 2,348 in-school adolescents who participated in the survey, 20% (23% males, and 17% females) reported h...

This report aims to introduce the fundamental features of the free Jamovi software to academics in the field of educational measurement for use at undergraduate and graduate level research. As such, after introducing the R based interface and the integrated development environment, the core functions of Jamovi are presented, the installation for GNU7Linux, Windows, and MacOS is explained and screenshots of frequently conducted statistical analyses are provided. Additionally, the module support of Jamovi is presented, along with a use case scenario on developing further functionality for Jamovi using modules. Specifically, conducting meta-analysis and Bayesian statistics using modules in Jamovi are explained through examples.

Recent advances in statistical software have led to the rapid diffusion of new methods for modelling longitudinal data. Multilevel (also known as hierarchical or random effects) models for binary outcomes have generally been based on a logistic-normal specification, by analogy with earlier work for normally distributed data. The appropriate application and interpretation of these models remains somewhat unclear, especially when compared with the computationally more straightforward semiparametric or 'marginal' modelling (GEE) approaches. In this paper we pose two interrelated questions. First, what limits should be placed on the interpretation of the coefficients and inferences derived from random-effect models involving binary outcomes? Second, what diagnostic checks are appropriate for evaluating whether such random-effect models provide adequate fits to the data? We address these questions by means of an extended case study using data on adolescent smoking from a large cohort study. Bayesian estimation methods are used to fit a discrete-mixture alternative to the standard logistic-normal model, and posterior predictive checking is used to assess model fit. Surprising parallels in the parameter estimates from the logistic-normal and mixture models are described and used to question the interpretability of the socalled 'subject-specific' regression coefficients from the standard multilevel approach. Posterior predictive checks suggest a serious lack of fit of both multilevel models. The results do not provide final answers to the two questions posed, but we expect that lessons learned from the case study will provide general guidance for further investigation of these important issues.

The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice 2.9, which extends the functionality of mice 1.0 in several ways. In mice 2.9, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice 2.9 adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice 2.9 can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

A random-effects regression model is proposed for analysis of clustered data. Unlike ordinary regression analysis of clustered data, random-effects regression models do not assume that each observation is independent but do assume that data within clusters are dependent to some degree. The degree of this dependency is estimated along with estimates of the usual model parameters, thus adjusting these effects for the dependency resulting from the clustering of the data. A maximum marginal likelihood solution is described, and available statistical software for the model is discussed. An analysis of a dataset in which students are clustered within classrooms and schools is used to illustrate features of random-effects regression analysis, relative to both individual-level analysis that ignores the clustering of the data, and classroom-level analysis that aggregates the individual data.

Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in geographic information systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. Open source R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it straightforward to implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications.

Background: Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I 2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic. Methods: We review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity.

This article details a Bayesian analysis of the Nile river flow data, using a simple state space model. This allows the article to concentrate on implementation issues surrounding this model. For this data set, Metropolis-Hastings and Gibbs sampling algorithms are implemented in the programming language Ox. The Markov chain Monte Carlo methods only provide output conditioned upon the full data set. For filtered output, conditioning only on past observations, the particle filter is introduced. The sampling methods are flexible, and this advantage is used to extend the model to incorporate a stochastic volatility process. The volatility changes both in the Nile data, and for comparison also in daily S&P 500 return data, are investigated. The posterior density of parameters and states is found to provide information on which elements of the model are easily identifiable, and which elements are estimated with less precision.

hoi computes the Human Opportunity Index proposed by Barros et al (2008), including the Coverage of Basic Opportunities (P), the Dissimilarity Index (D), and the Human Opportunity Index (HOI). Analytical standard errors are reported for the P, D, and HOI. The program also computes two decompositions of changes of the HOI: (i) the scale and distribution decomposition Barros et al

Co-authoring Dofiles can be challenging as most Stata users have idiosyncratic preferences and methods for organizing and writing Dofiles. Which standards and practices can research teams adopt to improve the cohesion of this group work? This article proposes some best practices to overcome team research coordination issues adapting methods from software engineering and data science along with personal experience with group research. We prioritize improvements that increase efficiency of the team workflow by establishing global parameters and directories, standardizing communication between team members, and enabling reproducibility of results.

Using data for 91 large banks from 45 countries, this paper finds few differences in the extent, type, and pricing of SME loans across foreign, private, and government-owned banks, even though different bank ownership types apply different lending technologies and have different organizational structures. Instead, we find significant differences across banks in developed and developing countries, driven by differences in the economic, institutional, and legal environment, as opposed to by differences in lending technologies and organizational structures. Finally, the link between lending technologies, organizational structures, and SME financing is not consistent with the conventional view that SME lending is based on "relationship lending".

In the last decade with widespread use of quantitative analyses in medical research, close co-operation between statisticians and physicians has become essential from the experimental design through all phases of complex statistical analysis. On the other hand, easy-to-use statistical packages allow clinicians to perform basic statistical analyses themselves. Since the software they most commonly use does not perform in depth competing risk analysis, we recommend an add-on package for the R statistical software. We provide all the instructions for downloading it from internet and illustrate how to use it for analysis of a sample dataset of patients who underwent haematopoietic stem cell transplantation for acute leukaemia.

Copyright c 2002, 2004, 2008 by StataCorp LP All rights reserved. First edition 2002 Revised edition 2004 Second edition 2008 Published by Stata Press, 4905 Lakeway Drive, College Station, Texas 77845 Typeset in LATE X2ε Printed in the United States of America ...

Educators need to know how to motivate business students (i.e., future business practitioners) to learn and use statistical software, which can provide the practical skills necessary for business professionals to analyze data and make informed decisions. Using a sample of 207 online MBA students from an AACSB accredited university in the Midwest, a modified TAM model was examined using LISREL 8.80. The empirical results show that both computer attitude and statistical software self-efficacy have significant, positive effects on perceived usefulness. In addition, it was found that both perceived usefulness and perceived ease of use positively influence learners' intentions to use statistical software, whereas their anxiety with statistics has a significant, negative impact on perceived usefulness, perceived ease of use and behavioral intentions. Both theoretical and practical implications are discussed in this paper.

Copyright c 2002, 2004, 2008 by StataCorp LP All rights reserved. First edition 2002 Revised edition 2004 Second edition 2008 Published by Stata Press, 4905 Lakeway Drive, College Station, Texas 77845 Typeset in LATE X2ε Printed in the United States of America ...

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An Introduction to Survival Analysis Using Stata, Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines. The third edition has been updated for Stata 11, and it includes a new chapter on competing-risks analysis. This chapter describes the problems posed by competing events (events that impede the failure event of interest), and covers estimation of cause-specific hazards and cumulative incidence functions. Other enhancements include the handling of missing values by multiple imputation in Cox regression, a new-to-Stata-11 system for specifying categorical (factor) variables and their interactions, three additional diagnostic measures for Cox regression, and a more efficient syn...

Experiments were conducted to study the deformation behaviour of front wheel-tyre assembly of a motorcycle when subjected to frontal impact loading. Impact tests were conducted using an in-house developed pendulum impact test apparatus. Statistical software, Minitab version 13, was employed to support the entire experimental process. Some of the parameters that influence response of the motorcycle in a frontal crash, viz., impact speed, impact mass, inflation pressure level of the tyre, contact geometry of the striker and offset distance of impact location from the axle were varied during the experiments. A 2 5À1 V fractional factorial design was incorporated in the experimental investigation and is described in detail. Experimental results are presented in terms of the maximum residual crush sustained by the wheel, normalized area of deformation and dissipated impact energy. Regression analysis to relate the impact energy to maximum residual crush and normalized area of deformation was then performed. It was found that the deformation of the wheel-tyre assembly is well correlated to the dissipated impact energy. Factorial analysis was also performed to investigate the effect of individual parameters on response of the assembly. These are presented in factorial plots, followed by a brief discussion.

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Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.

Purpose -The purpose of this paper is to identify distinct market segments based on several environmental variables. Given the complexity and the range of variables that may determine the characterisation of these groups, as was made evident in the literature review, it was decided to focus this research mainly on analysing the environmental and demographic criteria. Design/methodology/approach -The data were collected through a survey of Portuguese consumers, aged over 18. The model of data collection was a survey conducted by self-administered questionnaire. A total of 887 were considered valid (the final sample). The questionnaire is composed of two sections. In the first part, data are collected about the demographic characteristics of respondents. The second part examines the environmental dimension (concern, affect, knowledge, environmentally friendly behaviours, information search, activism, green products buying behaviour, sensitivity to price, waste separation/recycling, perceived efficiency, scepticism, etc.). After collection, the data were statistically analysed and interpreted using the statistical software Statistical Package for Social Sciences 15.0. The data obtained were submitted to a multivariate statistical analysis, which included the following sequence of statistical treatment: factor analysis, cluster analysis and discriminant analysis. After this, a characterisation was made of the segments found.

Multilevel structural equation modeling (MSEM) is gaining popularity in the social sciences as a framework for estimating latent variable models in the presence of hierarchical data. In addition, we believe that MSEMs are quite helpful to psychological and educational inquiries. Many research papers have been published on technical developments in MSEM; however, we are not aware of any tutorials on how to properly implement these complex models. In addition, applied researchers may be unfamiliar with how to implement a Bayesian estimation approach to MSEM, despite the fact that it has distinct advantages in this modeling context (Depaoli and Clifton, 2015; Hox, van de Schoot, and Matthijsse, 2012). To that end, this paper serves as a nontechnical tutorial on the implementation and application of MSEM using both frequentist and Bayesian estimation methods. We demonstrate the implementation of MSEM with three motivating examples using data from the Program for International Student Assessment (PISA). In addition, we present a small Monte Carlo study to show how the estimation of MSEM is impacted by different types of priors for data structures similar to those presented in the PISA examples. We conclude with recommendations for applied researchers based on findings from the empirical examples and the simulation study. We also outline future methodological considerations in the context of Bayesian MSEM.

This introduction to the R package betareg is a (slightly) modified version of Cribari-Neto and Zeileis (2010), published in the Journal of Statistical Software. A follow-up paper with various extensions is Grün, Kosmidis, and Zeileis (2012) -a slightly modified version of which is also provided within the package as vignette("betareg-ext", package = "betareg")

I would like to creat an open-source R package that makes working with Eurostat and OECD symmetric input-output tables very easy. The aim is to make various comparisons, for example in backward linkages or employment multipliers accross regions easy to compute. My 0.2 version works with 7 types of Eurostat input-output tables from 2010. I will soon add Hungarian, Croatian and OECD standard types. At the moment the bulk data can be organized into tidy SIOTs and some multipliers (value added, tax, employment, if data permits) and coefficients can be calculated.

I would like to release the code soon on CRAN and publish the documentation in the Journal of Statistical Software. I am looking for cooperation to use the software or test ie, i.e. to make comparable input-output economics research.  I am mainly interested in creative and cultural industries, but the software works with any industry.  It would be particularly interesting to re-run already published cases and complement them with international comparison.

Please get in touch on [www.linkedin.com/in/antaldaniel/](https://www.linkedin.com/in/antaldaniel/) or in email.

This book is the second edition of Modern Multidimensional Scaling. The first edition came out in 1997. As the authors point out, earlier versions of the book (with different combinations of authors, and in various languages) have been around since 1981. It is interesting to connect the various versions of the book with the history of multidimensional scaling (from now on MDS). For various reasons, MDS originated in psychometrics, and it was developed initially by psychometricians or mathematicians and statisticians working in psychometrics. It was soon picked up by marketers and geographers, and quite a bit later by astronomers, geneticists, and chemists.

Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel, we introduce techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. The approach uses a moving window weighting technique, where localised models are found at target locations. Outputs are mapped to provide a useful exploratory tool into the nature of the data spatial heterogeneity. GWmodel includes: GW summary statistics, GW principal components analysis, GW regression, GW regression with a local ridge compensation, and GW regression for prediction; some of which are provided in basic and robust forms.

This paper has two main sections, the first of which presents a summarized review of the literature concerning previous studies on the implementation of ISO 9000 quality management systems (QMSs) both in global construction companies as well as in Indonesian construction firms, and the perceived correlation between organisational culture and QMS practices in the construction sector. The first section of the paper contributes to the development of the second section, which presents details of the research project being undertaken. Based on the fundamental questions that led to the development of the main research objectives, suitable research methods have been developed in order to meet these objectives. Primary data will be collected by use of a mixed methods approach, i.e., questionnaire surveys and focus group discussions/interviews in order to obtain opinions from respondents drawn from targeted ISO construction firms. Most of the data expected to be obtained will be in future be analyzed using statistical software then the findings will be discussed in order to ultimately develop a culture-based QMS framework.

Exploratory Multivariate Analysis by Example Using R François Husson, Sébastien Lê, Jérôme Pagès Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1439835807. 240 pp. USD 79.95 (P). http://factominer.free.fr/book/ As its title suggests, this is an R demonstration book in the vein of, for example, Faraway (2005) on linear models. By working through books such as these, if they are done well, one can quickly become interested and conversant, even fairly adept, in the particular field of statistics being studied and its modern applications. Since this is intended as an undergraduate text for non-statistician scientists, it is fairer to hold it to the former standard, by which it can be considered highly successful.

splm is an R package for the estimation and testing of various spatial panel data specifications. We consider the implementation of both maximum likelihood and generalized moments estimators in the context of fixed as well as random effects spatial panel data models. This paper is a general description of splm and all functionalities are illustrated using a well-known example taken from Munnell (1990) with productivity data on 48 US states observed over 17 years. We perform comparisons with other available software; and, when this is not possible, Monte Carlo results support our original implementation. PLEASE NOTICE JSS IS OPEN ACCESS: download from jstatsoft.org, this is vol. 47/1

You yourself, or what is the same, your experience is such ``coin'' that, while you aren't questioned, it rotates all the time in ``free flight''. And only when you answer the question the ``coin'' falls on one of the sides: ``Yes'' or ``No'' with the believability that your experience tells you.

GAMLSS is a general framework for fitting regression type models where the distribution of the response variable does not have to belong to the exponential family and includes highly skew and kurtotic continuous and discrete distribution. GAMLSS allows all the parameters of the distribution of the response variable to be modelled as linear/non-linear or smooth functions of the explanatory variables.