General-purpose Statistical Packages Research Articles

An Algorithm for Genetic Analysis of Full-Sib Datasets with Mixed-Model Software Lacking a Numerator Relationship Matrix Function, and a Comparison with Results from a Dedicated Genetic Software Package

Research Highlights: An algorithm is presented that allows for the analysis of full-sib genetic datasets using generalized mixed-model software programs. The algorithm produces variance component estimates, genetic parameter estimates, and Best Linear Unbiased Prediction (BLUP) solutions for genetic values that are, for all practical purposes, identical to those produced by dedicated genetic software packages. Background and Objectives: The objective of this manuscript is to demonstrate an approach with a simulated full-sib dataset representing a typical forest tree breeding population (40 parents, 80 full-sib crosses, 4 tests, and 6000 trees) using two widely available mixed-model packages. Materials and Methods: The algorithm involves artificially doubling the dataset, so that each observation is in the dataset twice, once with the original female and male parent identification, and once with the female and male parent identities switched. Five linear models were examined: two models using a dedicated genetic software program (ASREML) with the capacity to specify A or other pedigree-related functions, and three models with the doubled dataset and a parent (or sire) linear model (ASREML, SAS Proc Mixed, and R lme4). Results: The variance components, genetic parameters, and BLUPs of the parental breeding values, progeny breeding values, and full-sib family-specific combining abilities were compared. Genetic parameter estimates were essentially the same across all the analyses (e.g., the heritability ranged from h2 = 0.220 to 0.223, and the proportion of dominance variance ranged from d2 = 0.057 to 0.058). The correlations between the BLUPs from the baseline analysis (ASREML with an individual tree model) and the doubled-dataset/parent models using SAS Proc Mixed or R lme4 were never lower than R = 0.99997. Conclusions: The algorithm can be useful for analysts who need to analyze full-sib genetic datasets and who are familiar with general-purpose statistical packages, but less familiar with or lacking access to other software.

Using R Studio in Intermedia Agenda-Setting Studies: Computerizing Cross-Lagged Correlation Using Rozelle-Campbell Baseline

Today, the importance of reproducible and transparent research has been increased along with the use of large data sets in a multinational environment. Researchers using intermedia agenda-setting theory have also affected this trend by conducting studies using big data and social media data. The Rozelle-Campbell formula is widely used by such researchers to investigate media effects and their direction. “R” is a general purpose statistical software package used in many fields of research. It is licensed for a free and an open-source software. There are many researchers have released applications for general use in “R” after they have been introduced into the literature. The authors have written this informative paper as a reference text for users of R in communication studies. Our primary purpose is to provide users an easy way to learn how to perform using R and R Studio analysis to use in intermedia agenda-setting, without having to navigate through the extensive, and excessive documentation. Another purpose of this study is to simplify and to reduce analyzing steps in complex cross-lagged correlations (synchronous correlations, cross-lagged correlations and autocorrelations) and Rozelle-Campbell Baseline analysis. On the other hand, neither do we claim to provide the most convenient solution for the researchers. We have tried to provide a simple approach which is easily understandable for those with an understanding of statistics. We anticipate that this tool “R” will be particularly useful for intermediate users, who need or want to conduct their studies when retrieving from the big data. In addition, this article will encourage researchers to improve their analytic abilities, by providing a brief manual and tested examples. Following the existing literature, this study re-tests the results of previous four intermedia agenda-settings studies, which had been previously analyzed by SPSS Software Program or Microsoft Excel. The present study’s data analysis 100% has matched with original studies. The results suggest that researchers can use this R Studio scripts to conduct their intermedia agenda-setting analysis.

Introduction to Competing Risk Model in the Epidemiological Research

Background and aims: Chronic kidney disease (CKD) is a public health challenge worldwide, with adverse consequences of kidney failure, cardiovascular disease (CVD), and premature death. The CKD leads to the end-stage of renal disease (ESRD) if late/not diagnosed. Competing risk modeling is a major issue in epidemiology research. In epidemiological study, sometimes, inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In these situations, competing risk analysis is preferred to other models in survival analysis studies. The purpose of this study was to describe the bias resulting from the use of standard survival analysis to estimate the survival of a patient with ESRD and to provide alternate statistical methods considering the competing risk. Methods: In this retrospective study, 359 patients referred to the hemodialysis department of Shahid Ayatollah Ashrafi Esfahani hospital in Tehran, and underwent continuous hemodialysis for at least three months. Data were collected through patient’s medical history contained in the records (during 2011-2017). To evaluate the effects of research factors on the outcome, cause-specific hazard model and competing risk models were fitted. The data were analyzed using Stata (a general-purpose statistical software package) software, version 14 and SPSS software, version 21, through descriptive and analytical statistics. Results: The median duration of follow-up was 3.12 years and mean age at ESRD diagnosis was 66.47 years old. Each year increase in age was associated with a 98% increase in hazard of death. In this study, statistical analysis based on the competing risk model showed that age, age of diagnosis, level of education (under diploma), and body mass index (BMI) were significantly associated with death (hazard ratio [HR]=0.98, P<0.001, HR=0.99, P<0.001, HR=2.66, P=0.008, and HR=0.98, P<0.020, respectively). Conclusion: In analysis of competing risk data, it was found that providing both the results of the event of interest and those of competing risks were of importance. The Cox model, which ignored the competing risks, presented the different estimates and results as compared to the proportional sub-distribution hazards model. Thus, it was revealed that in the analysis of competing risks data, the sub-distribution proportion hazards model was more appropriate than the Cox model.

Information technologies as a means of studying the phenomenon of the individual socialization

The article is devoted to the problems of the use of information technologies (IT) in sociological research, in particular in the process of studying the phenomenon of socialization of the individual. The material consists of three parts, which highlight the essential characteristics of the category of socialization, the current trends in the use of information technology in sociology, and some areas of application of information technology in the process of studying the socialization of the individual.
 Socialization of personality is an important social-pedagogical phenomenon, as the process of human entry into society is often accompanied by problems. Currently, the relevance of intelligence on the problems of socialization has increased significantly, which is largely connected with social changes. The article presents the definition and the main features of the category "socialization", scientific approaches to the interpretation of this concept, as well as a brief overview of the methods of diagnosing the level of socialization of a person.
 The formation of a digital society led to the appearance of computing social sciences and the separation of digital sociology at the beginning of the XXIst century. The peculiarity of the modern period is the use of various sources of secondary sociological information, in particular those which are based on the objective recording of the real behavior of people. The gathering of sociological information takes place with the use of methods of content analysis, analysis of texts and social networks, etc. The vast majority of these methods involve the use of information technology. In addition, methodologies for working with data from Big Data and Data Mining are now widely used to gather and analyze large volumes of heterogeneous data and to generate new useful information on existing or potential interconnections between phenomena on this basis. So, sociology is now focused on the use of new information tools. At the same time, the use of traditional information and communication technologies is still remains of current interest (tools for conducting surveys, statistical analysis programs, etc.), which can significantly reduce the waste of time, reduce the probability of errors of different types, carry out the different methods of analysis.
 In the process of the socialization of a person studying a variety of surveys are widely used, the results of which must be systematized and analyzed. In order to collect such data, it is expedient to use the possibilities of the Internet, which allows reaching a large audience of respondents and reduce the waste of time both by respondents and researchers. Currently online surveys are often used by Google Forms, which provides tools for creating questionnaires of different types of questions, storing received replies, transferring them to spreadsheets in Google Drive, and initial analysis. In the case of increased requirements to the complexity and quality of the questionnaire, as well as the need to reach a large number of respondents, you should turn to specialized web resources, such as SurveyMonkey. The data obtained during the online survey can be analyzed using statistical programs (Statgraphics, Statistica, S-plus, SPSS, Systat, etc.). In particular, such programs provide an opportunity for cluster and factor analysis to classify the phenomena studied and identify the most influential factors.
 Consequently, important steps in the study of the phenomenon of socialization and the level of socialization of the individual are the collection of data (through online surveys using Google Forms) and their analysis using software (office spreadsheets, general-purpose statistical packages or specialized programs aimed at performing specific statistical procedures).

Validation and comparison of geostatistical and spline models for spatial stream networks

Scientists need appropriate spatial‐statistical models to account for the unique features of stream network data. Recent advances provide a growing methodological toolbox for modelling these data, but general‐purpose statistical software has only recently emerged, with little information about when to use different approaches. We implemented a simulation study to evaluate and validate geostatistical models that use continuous distances, and penalised spline models that use a finite discrete approximation for stream networks. Data were simulated from the geostatistical model, with performance measured by empirical prediction and fixed effects estimation. We found that both models were comparable in terms of squared error, with a slight advantage for the geostatistical models. Generally, both methods were unbiased and had valid confidence intervals. The most marked differences were found for confidence intervals on fixed‐effect parameter estimates, where, for small sample sizes, the spline models underestimated variance. However, the penalised spline models were always more computationally efficient, which may be important for real‐time prediction and estimation. Thus, decisions about which method to use must be influenced by the size and format of the data set, in addition to the characteristics of the environmental process and the modelling goals. ©2015 The Authors. Environmetrics published by John Wiley & Sons, Ltd.

Introduction to the Statistical Concepts Symposium Section: Selected Review Topics to Improve Our Understanding and Use of Statistics

There are three kinds of lies; lies, damned lies and statistics. Statistics is a body of methods for making wise decisions in the face of uncertainty. In order to shed light on the use and misuse of statistics in the agricultural and natural resource sciences, Agronomy Journal set about to provide its authors and readers with useful information on statistical approaches and strategies for conducting and publishing scientific information. As readers, researchers, reviewers, or as editors of scientific literature, we may have come across situations or articles where we had serious doubts whether the most appropriate method to analyze an experiment or set of experiments was used. Furthermore, ever-increasing computing power has enabled researchers to use more intensive and complex statistical methods with the help of new and updated general and special purpose statistical software programs always becoming available. With these more complex computations, we now also have the ability to design more advanced and efficient experiments. We as biological scientists may or may not enjoy or have the time or training for learning to properly design, program, and interpret these sophisticated tools, a set of activities that is itself a continually dynamic field of study. As scientific researchers, it is our charge to produce forward-thinking and meaningful experimentation with the most appropriate and accurate methodology available. With these thoughts in mind, Brent Godshalk, then Editor of the Agronomy Journal, and his Editorial Board recognized the need for communications on these topics at the 2011 Agronomy Journal Board meeting and chartered the Statistics Committee. The Agronomy Journal Statistics Committee set a goal to provide clear guidance on the appropriate use of effective statistical tools and procedures to Agronomy Journal readers and contributors; thereby allowing them to focus on and support their science with proper statistical approaches. To this end, the committee and the ASA Biometry and Statistical Computing Section co-sponsored the Symposium, “Statistical Concepts and Tools to Aid in Publishing Proper Research Conclusions” at the 2012 ASA, CSSA, and SSSA International Annual Meetings. Our second and major output is this series of articles in the Agronomy Journal. The articles will also be part of the “Special Sections” webpage (https://www.agronomy.org/publications/aj/special-sections). All of the software code that was presented in appendices in separate articles is also available in one file that can be assessed at https://www.agronomy.org/publications/aj/special-sections. The software code is also a supplemental file with this introduction article. These articles and the additional output of our committee are targeted to support scientists in key basic concepts and methodologies in statistics and biometry. We believe that many of us need support in these basic concepts as an effective means of complementing the trend to focus on supporting scientists to learn exceedingly more complex and advanced statistical tools and methodologies. Neither the committee nor the contributing authors pretend that this effort is comprehensive or will solve all the challenges of data analysis and interpretation facing the agricultural and natural resources research communities. Our goal is that Agronomy Journal contributors and others in the scientific community strive to learn and use the newer, more advanced statistical methodologies—but first are sure to understand and use the basic methodologies when these are more appropriate. We hope that each of you agree with the goal of striving to conduct, interpret, and communicate your excellent science in the best possible way. Each article in this series received robust reviews from several reviewers. More than 30 anonymous reviewers in all donated their time, and we thank them for the substantial contribution they have made to this series. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Classification of Sampling Methods and its Applications in Various Fields

Statistics deals with aspects of data including the planning of data collection in terms of the design of surveys and experiments. In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as all persons living in a country or every atom composing a crystal. Statistics is the mathematical science involving the collection, analysis and interpretation of data. A number of specialties have evolved to apply statistical theory and methods to various disciplines. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. In statistics, quality assurance, and survey methodology, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Statistics tools are necessary for the data analysis. The computer revolution has implications for the future of statistics with new emphasis on experimental and empirical statistics. A large number of both general and special purpose statistical software are now available. Results from probability theory and statistical theory are employed to guide practice. In business and medical research, sampling is widely used for gathering information about a population.

Evaluation of bond strength of resin cements using different general-purpose statistical software packages for two-parameter Weibull statistics

ObjectivesThis study evaluated and compared Weibull parameters of resin bond strength values using six different general-purpose statistical software packages for two-parameter Weibull distribution. MethodsTwo-hundred human teeth were randomly divided into 4 groups (n=50), prepared and bonded on dentin according to the manufacturers’ instructions using the following resin cements: (i) Variolink (VAN, conventional resin cement), (ii) Panavia21 (PAN, conventional resin cement), (iii) RelyX Unicem (RXU, self-adhesive resin cement) and (iv) G-Cem (GCM, self-adhesive resin cement). Subsequently, all specimens were stored in water for 24h at 37°C. Shear bond strength was measured and the data were analyzed using Anderson–Darling goodness-of-fit (MINITAB 16) and two-parameter Weibull statistics with the following statistical software packages: Excel 2011, SPSS 19, MINITAB 16, R 2.12.1, SAS 9.1.3. and STATA 11.2 (p≤0.05). Additionally, the three-parameter Weibull was fitted using MNITAB 16. ResultsTwo-parameter Weibull calculated with MINITAB and STATA can be compared using an omnibus test and using 95% CI. In SAS only 95% CI were directly obtained from the output. R provided no estimates of 95% CI. In both SAS and R the global comparison of the characteristic bond strength among groups is provided by means of the Weibull regression. EXCEL and SPSS provided no default information about 95% CI and no significance test for the comparison of Weibull parameters among the groups. In summary, conventional resin cement VAN showed the highest Weibull modulus and characteristic bond strength. SignificanceThere are discrepancies in the Weibull statistics depending on the software package and the estimation method. The information content in the default output provided by the software packages differs to very high extent.

Meta-analyses and Forest plots using a microsoft excel spreadsheet: step-by-step guide focusing on descriptive data analysis

BackgroundMeta-analyses are necessary to synthesize data obtained from primary research, and in many situations reviews of observational studies are the only available alternative. General purpose statistical packages can meta-analyze data, but usually require external macros or coding. Commercial specialist software is available, but may be expensive and focused in a particular type of primary data. Most available softwares have limitations in dealing with descriptive data, and the graphical display of summary statistics such as incidence and prevalence is unsatisfactory. Analyses can be conducted using Microsoft Excel, but there was no previous guide available.FindingsWe constructed a step-by-step guide to perform a meta-analysis in a Microsoft Excel spreadsheet, using either fixed-effect or random-effects models. We have also developed a second spreadsheet capable of producing customized forest plots.ConclusionsIt is possible to conduct a meta-analysis using only Microsoft Excel. More important, to our knowledge this is the first description of a method for producing a statistically adequate but graphically appealing forest plot summarizing descriptive data, using widely available software.

A characteristic of consolidation parameters of marine clay in Busan Area - South Korea

In this study the city of Busan was selected and investigated for a characteristic of consolidation parameters. Busan is one of representative cities in the southern coast of Korea, where main harbors are located, and many geotechnical investigations were performed due to massive harbor constructions in the past. Consolidation parameters, including pre-consolidation pressure, compression index (Cc), swelling index (Cs), overconsolidation ratio, vertical and horizontal consolidation coefficient (Cv, Ch), vertical and horizontal permeability coefficients (Kv, Kh), were investigated by using the general-purpose statistical analysis software (SPSS) with collected data from laboratory tests and field studies. A correlation between these parameters was evaluated from statistically processed data through regression analysis after removing outliers. As a result, it was shown that the relationship between swelling index and compression index was Cs = Cc/8 and correction constant (modified compression index/compression index) Cc’ = 1.1471Cc(lab). However, there was no relation with outliers. It was also shown that overconsolidation ratio with exponential decay formula, consolidation and permeability coefficients with rational function formula had the highest coefficient of determination. Key words: Consolidation parameters, correlation, regression analysis, coefficient of determination.

Development of a System for the Evaluation of the Teaching Qualities of Anesthesiology Faculty

GRADUATE medical education is one of the core missions of academic medical centers, wherein medical specialists are responsible for teaching and supervising their future colleagues. However, being a medical specialist is no longer a sufficient qualification or proxy for competence in medical education aimed at training residents. This is particularly true given the modernization requirements for competency-based teaching and training promoted by accreditation institutions in some countries (such as the Accreditation for Council for Graduate Medical Education in the United States). These modernization efforts accelerate faculty development of clinicianeducators needed to achieve and maintain the highest standard of postgraduate medical education. An effective faculty development track should include measuring medical teaching effectiveness. This requires valid and reliable instruments, as well as providing the findings in a clear and concise format to faculty. Several studies have found that systematic and constructive feedback can result in improved teaching. There are few published and validated evaluation systems or even instruments aimed at supporting the graduate medical education qualities of clinical faculty. In anesthesiology, there are few published instruments and systems, and the existing ones tend to focus on faculty evaluation by residents only without any self-evaluations by faculty. To ensure actual behavioral change, individuals must usually undergo a stepwise change process. Evaluation insights obtained from feedback should be followed by creating positive intentions to change, trying out new behaviors and integrating them into practice. Supporting this change process has been shown to be effective. To support the specialty-specific evaluation of teaching qualities of anesthesiology faculty in an academic medical center, we developed the System for Evaluation of Teaching Qualities (SETQ) comprising (1) a Webbased self-evaluation by faculty, (2) a Web-based residents’ evaluation of faculty, (3) individualized faculty feedback, and (4) individualized faculty follow-up support. This paper has three main objectives: (1) to investigate the psychometric properties of the two instruments underlying the SETQ system, (2) to explore the relationship between residents’ evaluation and faculty self-evaluation, and (3) to gauge the feasibility of reliably using residents’ evaluation of faculty by estimating the number of such evaluations needed per faculty. We also place these objectives in context by describing SETQ. SETQ was initially developed in the anesthesiology department of a large academic medical center that has over 7,000 staff (including about 500 faculty and 400 residents) in the Netherlands. It was later expanded to include specialty-specific modules for internal medicine, surgery, and obstetrics and gynecology. At the time of writing, most of the remaining specialties have signed up for SETQ, resulting in more than 90% faculty coverage in 2009. SETQ is receiving nationwide attention.

Analyzing Longitudinal Data With the Linear Mixed Models Procedure in SPSS

Many applied researchers analyzing longitudinal data share a common misconception: that specialized statistical software is necessary to fit hierarchical linear models (also known as linear mixed models [LMMs], or multilevel models) to longitudinal data sets. Although several specialized statistical software programs of high quality are available that allow researchers to fit these models to longitudinal data sets (e.g., HLM), rapid advances in general purpose statistical software packages have recently enabled analysts to fit these same models when using preferred packages that also enable other more common analyses. One of these general purpose statistical packages is SPSS, which includes a very flexible and powerful procedure for fitting LMMs to longitudinal data sets with continuous outcomes. This article aims to present readers with a practical discussion of how to analyze longitudinal data using the LMMs procedure in the SPSS statistical software package.

A Closer Examination of Subpopulation Analysis of Complex-Sample Survey Data

In recent years, general-purpose statistical software packages have incorporated new procedures that feature several useful options for design-based analysis of complex-sample survey data. A common and frequently desired technique for analysis of survey data in practice is the restriction of estimation to a subpopulation of interest. These subpopulations are often referred to interchangeably in a variety of fields as subclasses, subgroups, and domains. In this article, we consider two approaches that analysts of complex-sample survey data can follow when analyzing subpopulations; we also consider the implications of each approach for estimation and inference. We then present examples of both approaches, using selected procedures in Stata to analyze data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). We conclude with important considerations for subpopulation analyses and a summary of suggestions for practice.

Facilitating Content Analysis in Tourism Research

The article proposes a new methodological approach to facilitate content analysis of electronic textual data in a more efficient and transparent way. The textual data are processed iteratively by two software products, CATPAC and WORDER. This approach permits smoothing of the original textual data, identification of the variables of interest, frequency count of the occurrences of these variables in the texts being processed, storage of frequency results in general purpose statistical packages, and subsequent dimensional reduction of word-frequency data by means of factor analysis. Application of this methodology is illustrated on three examples of destination-image studies, which cover content analyses of open-ended responses to e-survey questions, texts from tourism Web sites, and newspaper articles. Advantages and disadvantages of the proposed research technique, its contribution to tourism studies, and the place of the approach within the quantitative paradigm of content analysis are also discussed.

Methodological and computational considerations for multiple correlation analysis

The squared multiple correlation coefficient has been widely employed to assess the goodness-of-fit of linear regression models in many applications. Although there are numerous published sources that present inferential issues and computing algorithms for multinormal correlation models, the statistical procedure for testing substantive significance by specifying the nonzero-effect null hypothesis has received little attention. This article emphasizes the importance of determining whether the squared multiple correlation coefficient is small or large in comparison with some prescribed standard and develops corresponding Excel worksheets that facilitate the implementation of various aspects of the suggested significance tests. In view of the extensive accessibility of Microsoft Excel software and the ultimate convenience of general-purpose statistical packages, the associated computer routines for interval estimation, power calculation, a nd samplesize determination are alsoprovided for completeness. The statistical methods and available programs of multiple correlation analysis described in this article purport to enhance pedagogical presentation in academic curricula and practical application in psychological research.

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

BackgroundSeveral malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana.ResultsOf 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country.ConclusionWe have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.

Model‐based clustering using S‐PLUS

Cluster analysis can be used to identify homogenous subgroups in many fields, including psychology and psychiatry. However, most clustering methods implemented in general-purpose statistical packages are heuristic and can be criticized in principle for their lack of an underlying statistical model. Furthermore correlations between variables are generally ignored by standard methods. The question addressed here is whether currently available commercial software (S-PLUS), which provides model-based methods for clustering correlated continuous data, should be used for clustering data derived from questionnaires. Such data may be either continuous or ordinal in nature and typically exhibit correlations. Performance is assessed in this study on simulated data sets containing distinct multivariate normal subpopulations, both before and after mapping the simulated data onto an ordinal scale. A practical example showing how correlated data can be cluster-analysed using these methods is given. The conclusion is that model-based methods are certainly worthwhile for continuous data. However, their benefit, in particular their ability to deal with correlated data, is not marked for ordinal data. Simpler methods such as Ward's method may be almost as effective in this situation.

Using SAS PROC NLMIXED to fit item response theory models

Researchers routinely construct tests or questionnaires containing a set of items that measure personality traits, cognitive abilities, political attitudes, and so forth. Typically, responses to these items are scored in discrete categories, such as points on a Likert scale or a choice out of several mutually exclusive alternatives. Item response theory (IRT) explains observed responses to items on a test (questionnaire) by a person's unobserved trait, ability, or attitude. Although applications of IRT modeling have increased considerably because of its utility in developing and assessing measuring instruments, IRT modeling has not been fully integrated into the curriculum of colleges and universities, mainly because existing general purpose statistical packages do not provide built-in routines with which to perform IRT modeling. Recent advances in statistical theory and the incorporation of those advances into general purpose statistical software such as the Statistical Analysis System (SAS) allow researchers to analyze measurement data by using a class of models known as generalized linear mixed effects models (McCulloch & Searle, 2001), which include IRT models as special cases. The purpose of this article is to demonstrate the generality and flexibility of using SAS to estimate IRT model parameters. With real data examples, we illustrate the implementations of a variety of IRT models for dichotomous, polytomous, and nominal responses. Since SAS is widely available in educational institutions, it is hoped that this article will contribute to the spread of IRT modeling in quantitative courses.

Regression analysis of multiple source and multiple informant data from complex survey samples

In this tutorial, we describe regression-based methods for analysing multiple source data arising from complex sample survey designs. We use the term 'multiple-source' data to encompass all cases where data are simultaneously obtained from multiple informants, or raters (e.g. self-reports, family members, health care providers, administrators) or via different/parallel instruments, indicators or methods (e.g. symptom rating scales, standardized diagnostic interviews, or clinical diagnoses). We review regression models for analysing multiple source risk factors or multiple source outcomes and show that they can be considered special cases of generalized linear models, albeit with correlated outcomes. We show how these methods can be extended to handle the common survey features of stratification, clustering, and sampling weights. We describe how to fit regression models with multiple source reports derived from complex sample surveys using general purpose statistical software. Finally, the methods are illustrated using data from two studies: the Stirling County Study and the Eastern Connecticut Child Survey.

Review of Stata 7

Journal of Applied EconometricsVolume 16, Issue 5 p. 637-646 Software Review Review of Stata 7 Stanislav Kolenikov, Corresponding Author Stanislav Kolenikov skolenik@unc.edu Department of Statistics, University of North Carolina, Chapel Hill, NC 27599-3260, USA and Centre for Economic and Financial Research, Moscow, RussiaDepartment of Statistics, 117 New West, Cameron Avenue, University of North Carolina, Chapel Hill, NC, 27599-3260, USASearch for more papers by this author Stanislav Kolenikov, Corresponding Author Stanislav Kolenikov skolenik@unc.edu Department of Statistics, University of North Carolina, Chapel Hill, NC 27599-3260, USA and Centre for Economic and Financial Research, Moscow, RussiaDepartment of Statistics, 117 New West, Cameron Avenue, University of North Carolina, Chapel Hill, NC, 27599-3260, USASearch for more papers by this author First published: 22 October 2001 https://doi.org/10.1002/jae.631Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume16, Issue5September/October 2001Pages 637-646 RelatedInformation