BACKGROUND: Migraine imposes significant burden on patients, their families and health care systems. In this study, we compared episodic to chronic migraine sufferers to determine if migraine status predicted headache-related disability, health-related quality of life (HRQoL) and health care resource utilization. METHODS: A Web-based survey was administered to panelists from nine countries. Participants were classified as having chronic migraine (CM), episodic migraine (EM) or neither using a validated questionnaire. Data collected and then analyzed included sociodemographics, clinical characteristics, Migraine Disability Assessment, Migraine-Specific Quality of Life v2.1, Patient Health Questionnaire and health care resource utilization. FINDINGS: Of the respondents, 5.7% had CM and 94.3% had EM, with CM patients reporting significantly more severe disability, lower HRQoL, higher levels of anxiety and depression and greater health care resource utilization compared to those with EM. INTERPRETATION: These results provide evidence that will enhance our understanding of the factors driving health care costs and will contribute to development of cost-effective health care strategies.
Unrivalled in the way it makes the teaching of statistics compelling and accessible to even the most anxious of students, the only statistics textbook you and your students will ever need just got better! Andy Field's comprehensive and bestselling Discovering Statistics Using SPSS 4th Edition takes students from introductory statistical concepts through very advanced concepts, incorporating SPSS throughout. The Fourth Edition focuses on providing essential content updates, better accessibility to key features, more instructor resources, and more content specific to select disciplines. It also incorporates powerful new digital developments on the textbook's companion website(visit sagepub.com for more information). WebAssign The Fourth Edition will be available on WebAssign, allowing instructors to produce and manage assignments with their studnets online using a grade book that allows them to track and monitor students' progress. Students receive unlimited practice using a combination of approximately 2000 multiple choice and algorithmic questions. WebAssign provided students with instant feedback and links directly to the accompanying eBook section where the concept was covered, allowing students to find the correct solution. SAGE MobileStudy SAGE MobileStudy allows students equipped with smartphones and tablets to access select material, such as Cramming Sam's Study Tips, anywhere they receive mobile service. With QR codes included throughout the text, it's easy for students to get right to the section they need to study, allowing them to continue their study from virtually anywhere, even when they are away from thier printed copy of the text. Click here to preview the MobileStudy site (available late spring 2013). Education and Sport Sciences instructor support materials with enhanced ones for Psychology, Business and Management and the Health sciences make the book even more relevant to a wider range of subjects across the social sciences and where statistics is taught to a cross-disciplinary audience. Major Updates to the 4th Edition Fully compatible with recent SPSS releases up to and including version 20.0 Exciting new characters, including statistical cult leader Oditi, who provides students access to interesting and helpful video clips to illustrate statistical and SPSS concepts, and Confusious, who helps students clarify confusing quantitative terminology New discipline specific support matierlas have been added for Education, Sports Sciences, Psychology, Business & Management, and Health Sciences, making the book even more relevant to a wider range of subjects across the Social, Behavioral, and Health Sciences is taught to an interdisciplinary audience. An enhanced Companion Website (available late spring 2013) offers a wealth of material that can be used in conjunction with the textbook, including: PowerPoints Testbanks Answers to the Smart Alex tasks at the end of each chapter Datafiles for testing problems in SPSS Flashcards of key concepts Self-assessment multiple-choice questions Online videos of key statistical and SPSS procedures
Note: Includes bibliographical references, 3 appendixes and 2 indexes.- Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08
Bab 1 : Skala Pengukuran dan Metode Analisis Data Bab 2 : Pengnalan Program SPSS, Aplikasi Statistik Deskriptif dan Crosstab. Bab 3 : Data Screening dan Transformasi data Bab 4 : Uji Validitas dan Reliabilitas Suatu Konstruk Bab 5 : Uji Beda, ANOVA, ANCOVA dan MANOVA Bab 6 : Analisis Regresi Bab 7 : Uji Asumsi Klasik Bab 8 : Regresi dengan Asumsi Klasik, Variabel Dummy dan Chow test Bab 9 : Model Regresi dengan Bentuk Fungsional Bab 10 : Analisis Regresi Moderasi Bab 11 : Regresi dengan Variabel Mediator Bab 12 : Regresi dengan Variabel Moderator Bab 13 : Analisis Diskriminan Bab 14 : Logistic Regression Bab 15 : Korelasi Kanonikal Bab 16 : Analisis Conjoint Bab 17 : Analisis Faktor Bab 18 : Analisis Kluster Bab 19 : Multidimensional Scaling Bab 20 : Loglinear Analisis
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This article describes recent research in subjective usability measurement at IBM, focused on evaluating the psychometric properties of questionnaires designed for use in scenario‐based usability evaluation. The questionnaires address evaluation at both a global overall system level and at a more detailed scenario level. The primary goals of this article are to (a) discuss the psychometric characteristics of IBM questionnaires that measure user satisfaction with computer system usability, and (b) provide the questionnaires, with administration and scoring instructions. For scenario‐level measurement, the 3‐item After‐Scenario Questionnaire (ASQ) has excellent internal consistency, with coefficient alphas across a set of scenarios ranging from .90 to .96. For more global assessment, the Post‐Study System Usability Questionnaire (PSSUQ) also has excellent internal consistency, with an overall coefficient alpha of .97. Preliminary principal factor analysis of 48 PSSUQ questionnaires suggested the presence of three factors named, after varimax rotation, System Usefulness, Information Quality, and Interface Quality, with corresponding coefficient alphas of .96, .91, and .91. Evaluation of 377 PSSUQ questionnaires (modified to allow mailing to respondents in their offices and referred to as the Computer System Usability Questionnaire, or CSUQ) confirmed the structure of the preliminary principal factor analysis. Consequently, usability practitioners can use these questionnaires to help them measure users’ satisfaction with the usability of computer systems in the context of scenario‐based usability studies.
IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference, sixteenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Output for each procedure is explained and illustrated, and every output term is defined. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. This book covers the basics of statistical analysis and addresses more advanced topics such as multi-dimensional scaling, factor analysis, discriminant analysis, measures of internal consistency, MANOVA (between- and within-subjects), cluster analysis, Log-linear models, logistic regression and a chapter describing residuals. Back matter includes a description of data files used in exercises, an exhaustive glossary, suggestions for further reading and a comprehensive index. IMB SPSS Statistics 26 Step by Step is distributed in 85 countries, has been an academic best seller through most of the earlier editions, and has proved invaluable aid to thousands of researchers and students. New to this edition: Screenshots, explanations, and step-by-step boxes have been fully updated to reflect SPSS 26 How to handle missing data has been revised and expanded and now includes a detailed explanation of how to create regression equations to replace missing data More explicit coverage of how to report APA style statistics; this primarily shows up in the Output sections of Chapters 6 through 16, though changes have been made throughout the text.
Daftar Isi: Bab1 : Skala Pengukuran dan Metode Analisis Data Bab 2: Pengenalan Program SPSS, Aplikasi Statistika Deskriptif dan Uji Beda T-Test Bab 3: Data Screening dan Transpormasi Data Bab 4: Uji Reliabilitas dan Validitas Suatu Konstruk Atau Konsep Bab 5: Uji Beda T-Test, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), Dan Multiple Analysis of Variance (MANOVA) Bab 6: Analisis Regresi Bab 7: Uji Asumsi Klasik Bab 8: Regresi Dengan Uji Asumsi Klasik, Variabel Dummy dan Chow Test Bab 9: Model Regresi dengan Bentuk Fungsional Bab 10: Model Regresi Moderasi/Moderated Regression Analysis (MRA) Bab 11: Regresi Dengan Variabel Mediator Atau Intervening, Analisis Jalur Path (Path Analysis) Bab 12: Regresi Dengan Variabel Moderator dan Mediator (MODMED) Bab 13: Analisis Diskriminan Bab 14: Logistic Regression Bab 15: Korelasi Kanonikal (Canonical Correlation) Bab 16: Analisis Conjoin Bab 17: Analisis Faktor Bab 18: Analisis Kluster (Cluster Analysis) Bab 19: Regresi Partial Least Squares (PLS)
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We present a simple log-linear reparameterization of IBM Model 2 that overcomes problems arising from Model 1’s strong assumptions and Model 2’s overparameterization. Efficient inference, likelihood evaluation, and parameter estimation algorithms are provided. Training the model is consistently ten times faster than Model 4. On three large-scale translation tasks, systems built using our alignment model outperform IBM Model 4. An open-source implementation of the alignment model described in this paper is available from http://github.com/clab/fast align .
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Micro Experimental Laboratory (MEL) is a third-generation integrated software system for experimental research. The researcher fills in forms, and MEL writes the experimental program, runs the experiments, and analyzes the data. MEL includes a form-based user interface, automatic programming, computer tutorials, a compiler, a real-time data acquisition system, database management, statistical analysis, and subject scheduling. It can perform most reaction time, questionnaire, and text comprehension experiments with little or no programming. It includes a Pascal-like programming language and can call routines written in standard languages. MEL operates on IBM PC compatible computers and supports most display controllers. MEL maintains millisecond timing with high-speed text and graphics presentation. MEL provides a systematic approach to dealing with nine concerns in running an experimental laboratory.
Scheduling jobs on the IBM SP2 system and many other distributed-memory MPPs is usually done by giving each job a partition of the machine for its exclusive use. Allocating such partitions in the order in which the jobs arrive (FCFS scheduling) is fair and predictable, but suffers from severe fragmentation, leading to low utilization. This situation led to the development of the EASY scheduler which uses aggressive backfilling: Small jobs are moved ahead to fill in holes in the schedule, provided they do not delay the first job in the queue. We compare this approach with a more conservative approach in which small jobs move ahead only if they do not delay any job in the queue and show that the relative performance of the two schemes depends on the workload. For workloads typical on SP2 systems, the aggressive approach is indeed better, but, for other workloads, both algorithms are similar. In addition, we study the sensitivity of backfilling to the accuracy of the runtime estimates provided by the users and find a very surprising result. Backfilling actually works better when users overestimate the runtime by a substantial factor.
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A report is presented of some results of an ongoing project using neural-network modeling and learning techniques to search for and decode nonlinear regularities in asset price movements. The author focuses on the case of IBM common stock daily returns. Having to deal with the salient features of economic data highlights the role to be played by statistical inference and requires modifications to standard learning techniques which may prove useful in other contexts.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110. Highlights of the new edition include: Updated throughout to reflect IBM SPSS Version 21. Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change and longitudinal experimental designs (Ch.5). Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental) over time (Ch.6). New examples specifying multiple latent constructs and parallel growth processes (Ch. 7). Discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures (Ch.1). The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and cross-classified data sets. Chapters 3 and 4 introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting common programming and modeling problems. Models for investigating individual and organizational change are presented in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 provides an illustration of multilevel models with cross-classified data structures. The book concludes with ways to expand on the various multilevel and longitudinal modeling techniques and issues when conducting multilevel analyses. It's ideal for courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology.
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Presents SPSS in a Clear and Simple Way IBM SPSS Statistics 21 Step by Step: A Simple Guide and Reference, 13/e, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners. This book also serves as an effective reference for anyone conducting data analysis. Exercises at the end of each chapter give students an opportunity to practice using SPSS. Updated to reflect SPSS Version 21.0.
We have developed a versatile program for the analysis of nucleic acid and protein sequences on the IBM Personal Computer. The program is interactive and self-instructing. It contains all the features generally found in sequence analysis programs on large computers, including extensive homology routines, as well as new procedures for the entry of sequence data. The program contains facilities to store and utilize the entire Nucleic Acid Sequence Data Bank. We have devised a new algorithm to find restriction enzyme sites, which allows our microcomputer program to find all sites on a small plasmid for 100 different enzymes in 1 to 2 minutes.
This comprehensive second edition offers readers a complete guide to carrying out research projects involving structural equation modeling (SEM).