An Introduction to Psychometric Theory with Applications in R
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Undergraduate Psychology Major Handbook: How to Get the Most out Your EIU Psychology Degree (Online) Department of Psychology Mission Statement
2018-2019 EIU DEPARTMENT OF PSYCHOLOGY Undergraduate Psychology Major Handbook: How to get the most out your EIU Psychology Degree (Online) Department of Psychology Mission Statement The Department of Psychology emphasizes the scientific study of behavioral, affective, and cognitive processes and their application to real-world issues by engaging students in integrative learning experiences such as undergraduate and graduate research, internships, and enhanced classroom experiences. Faculty excellence in areas of teaching, research, and service provides the context for collaborative student- centered learning experiences that promote inclusion and diverse perspectives. The department’s curriculum furthers the development of students’ critical thinking, communication skills, research skills, quantitative reasoning, and ethical behavior in preparation for responsible citizenship, careers, graduate study, and lifelong learning. Eastern Illinois University Department of Psychology 600 E Lincoln • 1151 Physical Sciences Phone 217.581.2127 • Fax 217.581.6764 Website: http://www.eiu.edu/onlinepsych/ Email: [email protected] Table of Contents Table of Contents .................................................................................................................. 2 Is Psychology the Right Major for Me? ................................................................................... 3 Department of Psychology Learning Goals ............................................................................. 3 What can I do with a Bachelor’s -
Introduction to the Student Handbook
INTRODUCTION TO THE STUDENT HANDBOOK This Handbook outlines all program policies and procedures and should be consulted when questions arise. Covered are topics ranging from degree requirements, practicum, comprehensive examinations, grades, student evaluation, internship, student rights, and official program policies. In addition to this Handbook, faculty and students should also consult the Graduate School website (umaine.edu/graduate) and Department's Graduate Student Rules for other important information and policies. Of course, students and faculty must also adhere to the APA 2002 Code of Ethics (amended in 2010) for Psychologists. Upon entry into the program, students sign a form acknowledging that they have access to this Handbook and pledging adherence to program, department, and university policies, as well as the ethics code. Emily A. P. Haigh Assistant Professor and Director of Clinical Training Clinical Student Handbook | Page 2 | Return to Table of Contents T ABLE OF CONTENTS INTRODUCTION TO THE STUDENT HANDBOOK 2 INTRODUCTION 6 MISSION STATEMENT 6 MODEL OF TRAINING 6 AIMS 7 GENERALIST TRAINING AND SPECIALTY EMPHASES 7 ADMISSIONS REQUIREMENTS 7 DEGREE REQUIREMENTS 8 OVERVIEW 8 ADVISORY COMMITTEE 8 COURSE REQUIREMENTS FOR STUDENTS ENTERING FALL 2019 AND BEYOND 9 General Clinical Track Courses 9 Sample Schedule by Year: General Clinical Track 11 Clinical Child Emphasis Courses 12 Sample Schedule by Year: Clinical Child Emphasis 13 Clinical Neuropsychology Emphasis Courses 14 Sample Schedule by -
Cluster Analysis for Gene Expression Data: a Survey
Cluster Analysis for Gene Expression Data: A Survey Daxin Jiang Chun Tang Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: djiang3, chuntang, azhang @cse.buffalo.edu Abstract DNA microarray technology has now made it possible to simultaneously monitor the expres- sion levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremen- dous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expres- sion data, and also new algorithms have recently been proposed specifically aiming at gene ex- pression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. -
Reliability Engineering: Today and Beyond
Reliability Engineering: Today and Beyond Keynote Talk at the 6th Annual Conference of the Institute for Quality and Reliability Tsinghua University People's Republic of China by Professor Mohammad Modarres Director, Center for Risk and Reliability Department of Mechanical Engineering Outline – A New Era in Reliability Engineering – Reliability Engineering Timeline and Research Frontiers – Prognostics and Health Management – Physics of Failure – Data-driven Approaches in PHM – Hybrid Methods – Conclusions New Era in Reliability Sciences and Engineering • Started as an afterthought analysis – In enduing years dismissed as a legitimate field of science and engineering – Worked with small data • Three advances transformed reliability into a legitimate science: – 1. Availability of inexpensive sensors and information systems – 2. Ability to better described physics of damage, degradation, and failure time using empirical and theoretical sciences – 3. Access to big data and PHM techniques for diagnosing faults and incipient failures • Today we can predict abnormalities, offer just-in-time remedies to avert failures, and making systems robust and resilient to failures Seventy Years of Reliability Engineering – Reliability Engineering Initiatives in 1950’s • Weakest link • Exponential life model • Reliability Block Diagrams (RBDs) – Beyond Exp. Dist. & Birth of System Reliability in 1960’s • Birth of Physics of Failure (POF) • Uses of more proper distributions (Weibull, etc.) • Reliability growth • Life testing • Failure Mode and Effect Analysis -
Psychological Statistics Monday, 5:30 - 8:15 Classroom: HPR 253
SYLLABUS Spring 2018 Course Information: PSYC 2354.002 Psychological Statistics Monday, 5:30 - 8:15 Classroom: HPR 253 Professor Information: William Goette, M.S. Email address: [email protected] Textbook Information: Heiman (2013). Basic Statistics for the Behavioral Sciences (7th Ed.). A student at UT-Tyler is not under any obligation to purchase a textbook from a university-affiliated bookstore. The same textbook may also be available from an independent retailer, including an online retailer. Course Catalog Description PSYC 2354: An introduction to descriptive and inferential statistical methods used in psychological research. Emphasis will be on hypothesis testing with t-tests, analysis of variance, correlation, and selected nonparametric techniques. Student Learning Outcomes & Assessments Upon successful completion of the course, the student will be able to … 1. Demonstrate an understanding of the differences between and uses of descriptive and inferential statistics. (BS/BA 6.0) 2. Demonstrate an understanding of the differences between parametric and nonparametric statistics (BS/BA 6.0) a. Define and distinguish between a population and a sample. b. Define and distinguish between statistics and parameters. c. Classify data with respect to the four levels of measurement. 3. Compute statistical tests manually (with a calculator) and interpret and explain results. (BS/BA 6.0) a. Compute and explain measures of central tendency and find the mean, median and mode of a sample and a population b. Compute and explain variability: range, variance and standard deviation c. Calculate and interpret standard z scores and information gained through normal distribution tables. d. Calculate and interpret correlation coefficients using the Pearson and the Spearman. -
Fatigue and Psychological Distress in the Working Population Psychometrics, Prevalence, and Correlates
Journal of Psychosomatic Research 52 (2002) 445–452 Fatigue and psychological distress in the working population Psychometrics, prevalence, and correlates Ute Bu¨ltmanna,*, Ijmert Kanta, Stanislav V. Kaslb, Anna J.H.M. Beurskensa, Piet A. van den Brandta aDepartment of Epidemiology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands bDepartment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USA Received 11 October 2000 Abstract Objective: The purposes of this study were: (1) to explore the distinct pattern of associations was found for fatigue vs. relationship between fatigue and psychological distress in the psychological distress with respect to demographic factors. The working population; (2) to examine associations with demographic prevalence was 22% for fatigue and 23% for psychological and health factors; and (3) to determine the prevalence of fatigue distress. Of the employees reporting fatigue, 43% had fatigue only, and psychological distress. Methods: Data were taken from whereas 57% had fatigue and psychological distress. Conclusions: 12,095 employees. Fatigue was measured with the Checklist The results indicate that fatigue and psychological distress are Individual Strength, and the General Health Questionnaire (GHQ) common in the working population. Although closely associated, was used to measure psychological distress. Results: Fatigue was there is some evidence suggesting that fatigue and psychological fairly well associated with psychological distress. A separation -
When Psychometrics Meets Epidemiology and the Studies V1.Pdf
When psychometrics meets epidemiology, and the studies which result! Tom Booth [email protected] Lecturer in Quantitative Research Methods, Department of Psychology, University of Edinburgh Who am I? • MSc and PhD in Organisational Psychology – ESRC AQM Scholarship • Manchester Business School, University of Manchester • Topic: Personality psychometrics • Post-doctoral Researcher • Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh. • Primary Research Topic: Cognitive ageing, brain imaging. • Lecturer Quantitative Research Methods • Department of Psychology, University of Edinburgh • Primary Research Topics: Individual differences and health; cognitive ability and brain imaging; Psychometric methods and assessment. Journey of a talk…. • Psychometrics: • Performance of likert-type response scales for personality data. • Murray, Booth & Molenaar (2015) • Epidemiology: • Allostatic load • Measurement: Booth, Starr & Deary (2013); (Unpublished) • Applications: Early life adversity (Unpublished) • Further applications Journey of a talk…. • Methodological interlude: • The issue of optimal time scales. • Individual differences and health: • Personality and Physical Health (review: Murray & Booth, 2015) • Personality, health behaviours and brain integrity (Booth, Mottus et al., 2014) • Looking forward Psychometrics My spiritual home… Middle response options Strongly Agree Agree Neither Agree nor Disagree Strong Disagree Disagree 1 2 3 4 5 Strongly Agree Agree Unsure Disagree Strong Disagree -
Biostatistics (BIOSTAT) 1
Biostatistics (BIOSTAT) 1 This course covers practical aspects of conducting a population- BIOSTATISTICS (BIOSTAT) based research study. Concepts include determining a study budget, setting a timeline, identifying study team members, setting a strategy BIOSTAT 301-0 Introduction to Epidemiology (1 Unit) for recruitment and retention, developing a data collection protocol This course introduces epidemiology and its uses for population health and monitoring data collection to ensure quality control and quality research. Concepts include measures of disease occurrence, common assurance. Students will demonstrate these skills by engaging in a sources and types of data, important study designs, sources of error in quarter-long group project to draft a Manual of Operations for a new epidemiologic studies and epidemiologic methods. "mock" population study. BIOSTAT 302-0 Introduction to Biostatistics (1 Unit) BIOSTAT 429-0 Systematic Review and Meta-Analysis in the Medical This course introduces principles of biostatistics and applications Sciences (1 Unit) of statistical methods in health and medical research. Concepts This course covers statistical methods for meta-analysis. Concepts include descriptive statistics, basic probability, probability distributions, include fixed-effects and random-effects models, measures of estimation, hypothesis testing, correlation and simple linear regression. heterogeneity, prediction intervals, meta regression, power assessment, BIOSTAT 303-0 Probability (1 Unit) subgroup analysis and assessment of publication -
Big Data for Reliability Engineering: Threat and Opportunity
Reliability, February 2016 Big Data for Reliability Engineering: Threat and Opportunity Vitali Volovoi Independent Consultant [email protected] more recently, analytics). It shares with the rest of the fields Abstract - The confluence of several technologies promises under this umbrella the need to abstract away most stormy waters ahead for reliability engineering. News reports domain-specific information, and to use tools that are mainly are full of buzzwords relevant to the future of the field—Big domain-independent1. As a result, it increasingly shares the Data, the Internet of Things, predictive and prescriptive lingua franca of modern systems engineering—probability and analytics—the sexier sisters of reliability engineering, both statistics that are required to balance the otherwise orderly and exciting and threatening. Can we reliability engineers join the deterministic engineering world. party and suddenly become popular (and better paid), or are And yet, reliability engineering does not wear the fancy we at risk of being superseded and driven into obsolescence? clothes of its sisters. There is nothing privileged about it. It is This article argues that“big-picture” thinking, which is at the rarely studied in engineering schools, and it is definitely not core of the concept of the System of Systems, is key for a studied in business schools! Instead, it is perceived as a bright future for reliability engineering. necessary evil (especially if the reliability issues in question are safety-related). The community of reliability engineers Keywords - System of Systems, complex systems, Big Data, consists of engineers from other fields who were mainly Internet of Things, industrial internet, predictive analytics, trained on the job (instead of receiving formal degrees in the prescriptive analytics field). -
Interactive Statistical Graphics/ When Charts Come to Life
Titel Event, Date Author Affiliation Interactive Statistical Graphics When Charts come to Life [email protected] www.theusRus.de Telefónica Germany Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 2 www.theusRus.de What I do not talk about … Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 3 www.theusRus.de … still not what I mean. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. 1973 PRIM-9 Tukey et al. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. -
Cluster Analysis Or Clustering Is a Common Technique for Statistical
IOSR Journal of Engineering Apr. 2012, Vol. 2(4) pp: 719-725 AN OVERVIEW ON CLUSTERING METHODS T. Soni Madhulatha Associate Professor, Alluri Institute of Management Sciences, Warangal. ABSTRACT Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations. Keywords: Clustering, hierarchical algorithm, partitional algorithm, distance measure, I. INTRODUCTION finding the length of the hypotenuse in a triangle; that is, it Clustering can be considered the most important is the distance "as the crow flies." A review of cluster unsupervised learning problem; so, as every other problem analysis in health psychology research found that the most of this kind, it deals with finding a structure in a collection common distance measure in published studies in that of unlabeled data. A cluster is therefore a collection of research area is the Euclidean distance or the squared objects which are “similar” between them and are Euclidean distance. “dissimilar” to the objects belonging to other clusters. Besides the term data clustering as synonyms like cluster The Manhattan distance function computes the analysis, automatic classification, numerical taxonomy, distance that would be traveled to get from one data point to botrology and typological analysis. the other if a grid-like path is followed. -
Cluster Analysis: What It Is and How to Use It Alyssa Wittle and Michael Stackhouse, Covance, Inc
PharmaSUG 2019 - Paper ST-183 Cluster Analysis: What It Is and How to Use It Alyssa Wittle and Michael Stackhouse, Covance, Inc. ABSTRACT A Cluster Analysis is a great way of looking across several related data points to find possible relationships within your data which you may not have expected. The basic approach of a cluster analysis is to do the following: transform the results of a series of related variables into a standardized value such as Z-scores, then combine these values and determine if there are trends across the data which may lend the data to divide into separate, distinct groups, or "clusters". A cluster is assigned at a subject level, to be used as a grouping variable or even as a response variable. Once these clusters have been determined and assigned, they can be used in your analysis model to observe if there is a significant difference between the results of these clusters within various parameters. For example, is a certain age group more likely to give more positive answers across all questionnaires in a study or integration? Cluster analysis can also be a good way of determining exploratory endpoints or focusing an analysis on a certain number of categories for a set of variables. This paper will instruct on approaches to a clustering analysis, how the results can be interpreted, and how clusters can be determined and analyzed using several programming methods and languages, including SAS, Python and R. Examples of clustering analyses and their interpretations will also be provided. INTRODUCTION A cluster analysis is a multivariate data exploration method gaining popularity in the industry.