Language: English Course level: Doctoral level
|
|
- Griselda Sarah Newton
- 5 years ago
- Views:
Transcription
1 Course Description: Bayesian data analysis and multilevel modeling 7.5hp Course leader Marco Tullio Liuzza Phone: Teachers Marco Tullio Liuzza Phone: Mats Nilsson Phone: Stefan Wiens Phone: Course administrator Monika Karlsson Phone: Examiner Marco Tullio Liuzza Phone: Language: English Course level: Doctoral level Eligibility criteria: Accepted for studies at doctoral level within social sciences including public health Main field of study: Psychology Host department: Department of Psychology, Stockholm University Sign up: Note that the number of participants will be limited and registration is on a first-come, first-served basis.
2 Bayesian data analysis and multilevel modeling The course will start with a general seminar to discuss the benefits of Bayesian approaches to data analysis for Psychology scholars. The first week of the course covers Bayesian analysis: its theory, implementation in software (R and JASP), and applied use on the students own data. Even though other alternative approaches will be presented, we will focus on the Bayes Factor approach to hypothesis testing, because this approach is applicable to most of the research designs in Psychology, and is implemented in popular R packages such as BayesFactor (Morey and Rouder, 2015) and online calculators. Besides the theoretical aspects, the course will offer actual programming in R to show how to generate priors, likelihood and posteriors on the basis of practical examples. Students will be introduced to the user-friendly, free software JASP (JASP Team, 2016). The final goal of the first week is to allow students to perform Bayesian data analyses on their own data and to plan a design using a Sequential Bayes Factor Design analysis (Schönbrodt and Wagemakers, 2016). The second week of the course covers multilevel models (MLMs) because these are more robust to unequal sample sizes, missing data, and extreme cases, as estimation of individual level and group level parameters inform each other in an updating process (e.g. German & Hill, 2007). Also, MLM can be nicely combined with Bayesian inference (McElreath, 2016). Thus, Bayesian approaches are useful to compare different multilevel models. Since All models are wrong, but some are useful (Box, 1976), model selection requires a good balance between correctly describing the data and predicting new ones. Statistical inference is not a universal procedure that provides a surrogate of objective truth (Gigerenzer, & Marewski 2014). Abandoning this misbelief forces scholars to think carefully about how they are testing their hypotheses and what they can reasonably infer from their data. To illustrate the advantage of MLM, categorical data are often analyzed as accuracy rates (in percent), but Jaeger (2008) has shown that this approach can lead to spurious results, as linear models such ANOVAs rest on assumptions that are not tenable when dealing with categorical data. These issues are avoided in Generalized Linear Mixed Models (AKA Generalized Multilevel Models, GMLMs) for binomially distributed outcome. GMLMs combine the advantages of ordinary logit models with the ability to account for random subject and item effects at once. Also, the use of GMLMs provides a much more flexible approach that allows to model continuous variables on a trial-by-trial basis.
3 Therefore, the second week of the course introduces multilevel models (MLMs) more broadly and teaches how to implement them in the popular R package lme4 (Bates, Maechler, Bolker, Walker, 2015). Particular emphasis will be placed on (re)introducing linear models from scratch and showing how all the most popular analytic approaches used in Psychology (from t-tests to ANOVAs and ANCOVAs) are nothing but specific linear models. We will then focus on the random effects (intercepts, slopes and their covariance) in linear models and show how to model them in lme4. Last, we will reconcile the topics treated during the first week and multilevel modeling by setting and updating priors on MLMs parameters and will show how to adopt a Bayesian approach in model comparison. Expected learning outcomes After the course, students will be able to compute Bayes Factors for their own datasets and to perform a Bayesian multilevel model analysis. Examination At the end of each week participants will have to pass a quiz to assess their knowledge of the basic concepts of the course. One week after the end of the course participants will have to submit a final, written assignment that will require the analysis of real or simulated data which will be then reported as a results paragraph. Participants need to submit commented code in R and JAGS/Stan. Grade and grade criteria The course is graded on a pass/fail basis: Pass: For a passing grade, the doctoral student has completed the examination requirements and thereby shown that the expected learning outcomes are achieved. Fail: The examination task has been solved insufficiently, in such a way that the expected learning outcomes are not met. Dates The course will occur on weeks 11 and 12 of the year See preliminary schedule below.
4 Reading lists Books Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press. Chapter 2. McElreath, R. (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Vol. 122). CRC Press. Articles Dienes, Z. (2011). Bayesian Versus Orthodox Statistics: Which Side Are You On? Perspectives on Psychological Science, 6(3), Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2016). How to become a Bayesian in eight easy steps: An annotated reading list. Manuscript submitted for publication. Freely available: e_a_bayesian_in_eight_easy_steps_an_annotated_reading_list Gelman, A., Hill, J., & Yajima, M. (2012). Why we (usually) don't have to worry about multiple comparisons. Journal of Research on Educational Effectiveness, 5(2), Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59(4), Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology General, 142(2), Lindley, D. V. (1993). The analysis of experimental data: The appreciation of tea and wine. Teaching Statistics, 15(1), Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin and Review, 16(2),
5 Rouder, J. N. (2014). Optional stopping: No problem for Bayesians. Psychonomic Bulletin and Review, 21(2), Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Aken, M. A. (2014). A gentle introduction to Bayesian analysis: Applications to developmental research. Child development, 85(3), Vandekerckhove, J., Matzke, D., & Wagenmakers, E. J. (2014). Model comparison and the principle of parsimony. Wagenmakers, E. J., Morey, R. D., & Lee, M. D. (2016). Bayesian Benefits for the Pragmatic Researcher. Current Directions in Psychological Science, 25(3), Wiens, S., & Nilsson, M. E. (2016). Performing Contrast Analysis in Factorial Designs From NHST to Confidence Intervals and Beyond. Educational and Psychological Measurement, Preliminary schedule Datum Dag Plats Tid Tema Lärare Undervisningstyp Tis GEL Introduction and Probability MTL Förel Ons GEL Simulations and Likelihood intervals MN Förel Tor GEL Bayesian approaches MTL Förel Fre GEL Bayes Factor SW Förel Mån GEL Bayes Factor seminar SW Handl.möte Tis GEL Linear regression MN Förel Ons GEL Mutilevel models MTL Förel Tor GEL Generalized Multilevel models MTL Förel Fre GEL Bayesian multilevel models MTL Förel Mån GEL Bayesian multilevel models MTL Handl.möte
Bayes Factors for t tests and one way Analysis of Variance; in R
Bayes Factors for t tests and one way Analysis of Variance; in R Dr. Jon Starkweather It may seem like small potatoes, but the Bayesian approach offers advantages even when the analysis to be run is not
More informationAB - Bayesian Analysis
Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2018 200 - FME - School of Mathematics and Statistics 715 - EIO - Department of Statistics and Operations Research MASTER'S DEGREE
More informationResponse to Comment on Cognitive Science in the field: Does exercising core mathematical concepts improve school readiness?
Response to Comment on Cognitive Science in the field: Does exercising core mathematical concepts improve school readiness? Authors: Moira R. Dillon 1 *, Rachael Meager 2, Joshua T. Dean 3, Harini Kannan
More informationBayesian alternatives for common null-hypothesis significance tests in psychiatry: A non-technical guide using JASP
Quintana and Williams Bayesian alternatives for common null-hypothesis significance tests in psychiatry: A non-technical guide using JASP Daniel S. Quintana 1* and Donald R. Williams 2 * Correspondence:
More informationEstimating Bayes Factors for Linear Models with Random Slopes. on Continuous Predictors
Running Head: BAYES FACTORS FOR LINEAR MIXED-EFFECTS MODELS 1 Estimating Bayes Factors for Linear Models with Random Slopes on Continuous Predictors Mirko Thalmann ab *, Marcel Niklaus a, & Klaus Oberauer
More informationA Bayes Factor for Replications of ANOVA Results
A Bayes Factor for Replications of ANOVA Results Christopher Harms 1,* 1 Rheinische Friedrich-Wilhelms-Universität Bonn, Germany * Email: christopher.harms@uni-bonn.de Abstract arxiv:1611.09341v2 [stat.ap]
More informationRecency order judgments in short term memory: Replication and extension of Hacker (1980)
Recency order judgments in short term memory: Replication and extension of Hacker (1980) Inder Singh and Marc W. Howard Center for Memory and Brain Department of Psychological and Brain Sciences Boston
More informationMaking Null Effects Informative: Statistical Techniques and Inferential Frameworks
Making Null Effects Informative: Statistical Techniques and Inferential Frameworks Christopher Harms a,b,* & Daniël Lakens b July 30, 2018 This pre-print represents the accepted version of the following
More informationSupplemental Materials for Learning absolute meaning from variable exemplars. 1. Additional analyses of participants responses in Experiments 1 and 2
Supplemental Materials for Learning absolute meaning from variable exemplars 1. Additional analyses of participants responses in Experiments 1 and 2 As discussed in Section 2.4, our analysis mainly focused
More informationTHE INTERPRETATION OF EFFECT SIZE IN PUBLISHED ARTICLES. Rink Hoekstra University of Groningen, The Netherlands
THE INTERPRETATION OF EFFECT SIZE IN PUBLISHED ARTICLES Rink University of Groningen, The Netherlands R.@rug.nl Significance testing has been criticized, among others, for encouraging researchers to focus
More informationAdvanced Bayesian Models for the Social Sciences. TA: Elizabeth Menninga (University of North Carolina, Chapel Hill)
Advanced Bayesian Models for the Social Sciences Instructors: Week 1&2: Skyler J. Cranmer Department of Political Science University of North Carolina, Chapel Hill skyler@unc.edu Week 3&4: Daniel Stegmueller
More informationWhy Mixed Effects Models?
Why Mixed Effects Models? Mixed Effects Models Recap/Intro Three issues with ANOVA Multiple random effects Categorical data Focus on fixed effects What mixed effects models do Random slopes Link functions
More informationData Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis Using Regression and Multilevel/Hierarchical Models ANDREW GELMAN Columbia University JENNIFER HILL Columbia University CAMBRIDGE UNIVERSITY PRESS Contents List of examples V a 9 e xv " Preface
More informationSupplementary Materials Order Matters: Alphabetizing In-Text Citations Biases Citation Rates Jeffrey R. Stevens and Juan F. Duque
Supplementary Materials Order Matters: Alphabetizing In-Text Citations Biases Citation Rates Jeffrey R. Stevens and Juan F. Duque Supplementary Analyses Article-Level Analyses For the article-level analyses,
More informationBAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS
Sara Garofalo Department of Psychiatry, University of Cambridge BAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS Overview Bayesian VS classical (NHST or Frequentist) statistical approaches Theoretical issues
More informationBayesian Mixture Modeling of Significant P Values: A Meta-Analytic Method to Estimate the Degree of Contamination from H 0
Bayesian Mixture Modeling of Significant P Values: A Meta-Analytic Method to Estimate the Degree of Contamination from H 0 Quentin Frederik Gronau 1, Monique Duizer 1, Marjan Bakker 2, & Eric-Jan Wagenmakers
More informationStatistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests
Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests Ruud Wetzels 1, Dora Matzke 1, Michael D. Lee 2, Jeffrey N. Rouder 3, Geoffrey J. Iverson 2, and Eric-Jan Wagenmakers
More informationAdvanced Bayesian Models for the Social Sciences
Advanced Bayesian Models for the Social Sciences Jeff Harden Department of Political Science, University of Colorado Boulder jeffrey.harden@colorado.edu Daniel Stegmueller Department of Government, University
More informationWhy Hypothesis Tests Are Essential for Psychological Science: A Comment on Cumming. University of Groningen. University of Missouri
Running head: TESTING VS. ESTIMATION Why Hypothesis Tests Are Essential for Psychological Science: A Comment on Cumming Richard D. Morey 1, Jeffrey N. Rouder 2, Josine Verhagen 3 and Eric-Jan Wagenmakers
More informationGuidelines for reviewers
Guidelines for reviewers Registered Reports are a form of empirical article in which the methods and proposed analyses are pre-registered and reviewed prior to research being conducted. This format of
More informationKeywords NHST; Bayesian inference; statistical analysis; mental disorders; fear learning
Europe PMC Funders Group Author Manuscript Published in final edited form as: J Exp Psychopathol. 2017 ; 8(2): 140 157. doi:10.5127/jep.057316. A Primer on Bayesian Analysis for Experimental Psychopathologists
More informationBayesian and Frequentist Approaches
Bayesian and Frequentist Approaches G. Jogesh Babu Penn State University http://sites.stat.psu.edu/ babu http://astrostatistics.psu.edu All models are wrong But some are useful George E. P. Box (son-in-law
More informationHow do we combine two treatment arm trials with multiple arms trials in IPD metaanalysis? An Illustration with College Drinking Interventions
1/29 How do we combine two treatment arm trials with multiple arms trials in IPD metaanalysis? An Illustration with College Drinking Interventions David Huh, PhD 1, Eun-Young Mun, PhD 2, & David C. Atkins,
More informationSome Examples of Using Bayesian Statistics in Modeling Human Cognition
Some Examples of Using Bayesian Statistics in Modeling Human Cognition Michael Lee Department of Cognitive Sciences University of California, Irvine mdlee@uci.edu http://faculty.sites.uci.edu/mdlee http://sites.uci.edu/memorydecisionlab
More informationInference beyond significance testing: a gentle primer of model based inference
Inference beyond significance testing: a gentle primer of model based inference Junpeng Lao, PhD Fribourg Day of Cognition 2017/10/04 https://www.nature.com/articles/s41562-017-0189-z An old tale: the
More informationUsing Ensembles of Cognitive Models to Answer Substantive Questions
Using Ensembles of Cognitive Models to Answer Substantive Questions Henrik Singmann (singmann@gmail.com) Department of Psychology, University of Zürich Binzmühlestrasse 14/, 8050 Zurich, Switzerland David
More informationEffect of Sample Size on Correlation and Regression Coefficients
Effect of Sample Size on Correlation and Regression Coefficients Swati Gupta 1 Research Scholar, Department of Education, Aligarh Muslim University, India Dr. Mamun Ali Naji Qasem 2 Faculty of Education,
More informationA Bayesian perspective on the Reproducibility Project: Psychology. Abstract. 1 Introduction 1
A Bayesian perspective on the Reproducibility Project: Psychology Alexander Etz 1 and Joachim Vandekerckhove 2,3,* 1 Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands 2 Department
More informationDan Byrd UC Office of the President
Dan Byrd UC Office of the President 1. OLS regression assumes that residuals (observed value- predicted value) are normally distributed and that each observation is independent from others and that the
More informationBayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm
Journal of Social and Development Sciences Vol. 4, No. 4, pp. 93-97, Apr 203 (ISSN 222-52) Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm Henry De-Graft Acquah University
More informationJake Bowers Wednesdays, 2-4pm 6648 Haven Hall ( ) CPS Phone is
Political Science 688 Applied Bayesian and Robust Statistical Methods in Political Research Winter 2005 http://www.umich.edu/ jwbowers/ps688.html Class in 7603 Haven Hall 10-12 Friday Instructor: Office
More informationUvA-DARE (Digital Academic Repository) Bayesian benefits with JASP Marsman, M.; Wagenmakers, E.M.
UvA-DARE (Digital Academic Repository) Bayesian benefits with JASP Marsman, M.; Wagenmakers, E.M. Published in: The European Journal of Developmental Psychology DOI: 10.1080/17405629.2016.1259614 Link
More informationFour reasons to prefer Bayesian over orthodox statistical analyses. Zoltan Dienes. University of Sussex. Neil Mclatchie. Lancaster University
Four reasons to prefer Bayesian over orthodox statistical analyses Zoltan Dienes University of Sussex Neil Mclatchie Lancaster University Corresponding author: Zoltan Dienes School of Psychology University
More informationMULTIPLE LINEAR REGRESSION 24.1 INTRODUCTION AND OBJECTIVES OBJECTIVES
24 MULTIPLE LINEAR REGRESSION 24.1 INTRODUCTION AND OBJECTIVES In the previous chapter, simple linear regression was used when you have one independent variable and one dependent variable. This chapter
More informationFor general queries, contact
Much of the work in Bayesian econometrics has focused on showing the value of Bayesian methods for parametric models (see, for example, Geweke (2005), Koop (2003), Li and Tobias (2011), and Rossi, Allenby,
More informationA Mini-conference on Bayesian Methods at Lund University 5th of February, 2016 Room C121, Lux building, Helgonavägen 3, Lund University.
A Mini-conference on Bayesian Methods at Lund University 5th of February, 2016 Room C121, Lux building, Helgonavägen 3, Lund University. www.lucs.lu.se/bayes-at-lund-2016/ Program 12.15 12.45 Welcome and
More informationRunning Head: BAYESIAN STATISTICS IN EDUCATIONAL RESEARCH 1. Bayesian statistics in educational research A look at the current state of affairs
Running Head: BAYESIAN STATISTICS IN EDUCATIONAL RESEARCH 1 Bayesian statistics in educational research A look at the current state of affairs Christoph König Friedrich Schiller University Jena, Germany
More informationANCOVA with Regression Homogeneity
ANCOVA with Regression Homogeneity The purpose of the study was to compare the effectiveness of two different treatments in two populations. Both treatments have been repeatedly shown to work better than
More informationUsing Bayes Factors to Get the Most out of Linear Regression: A Practical Guide Using R
MATHEMATICS Using Bayes Factors to Get the Most out of Linear Regression: A Practical Guide Using R ALEXANDER ETZ ABSTRACT This guide is for readers who want to make rich inferences from their data. It
More information2.75: 84% 2.5: 80% 2.25: 78% 2: 74% 1.75: 70% 1.5: 66% 1.25: 64% 1.0: 60% 0.5: 50% 0.25: 25% 0: 0%
Capstone Test (will consist of FOUR quizzes and the FINAL test grade will be an average of the four quizzes). Capstone #1: Review of Chapters 1-3 Capstone #2: Review of Chapter 4 Capstone #3: Review of
More informationUSING BAYES FACTORS TO EVALUATE EVIDENCE FOR NO EFFECT: EXAMPLES FROM THE SIPS PROJECT. Zoltan Dienes, School of Psychology, University of Sussex
USING BAYES FACTORS TO EVALUATE EVIDENCE FOR NO EFFECT: EXAMPLES FROM THE SIPS PROJECT Zoltan Dienes, School of Psychology, University of Sussex Simon Coulton, Centre for Health Services Studies, University
More informationPropensity Score Methods with Multilevel Data. March 19, 2014
Propensity Score Methods with Multilevel Data March 19, 2014 Multilevel data Data in medical care, health policy research and many other fields are often multilevel. Subjects are grouped in natural clusters,
More informationIntroduction to Multilevel Models for Longitudinal and Repeated Measures Data
Introduction to Multilevel Models for Longitudinal and Repeated Measures Data Today s Class: Features of longitudinal data Features of longitudinal models What can MLM do for you? What to expect in this
More informationXiaoyan(Iris) Lin. University of South Carolina Office: B LeConte College Fax: Columbia, SC, 29208
Xiaoyan(Iris) Lin Department of Statistics lin9@mailbox.sc.edu University of South Carolina Office: 803-777-3788 209B LeConte College Fax: 803-777-4048 Columbia, SC, 29208 Education Doctor of Philosophy
More informationCHAPTER - 6 STATISTICAL ANALYSIS. This chapter discusses inferential statistics, which use sample data to
CHAPTER - 6 STATISTICAL ANALYSIS 6.1 Introduction This chapter discusses inferential statistics, which use sample data to make decisions or inferences about population. Populations are group of interest
More informationBayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Psychon Bull Rev (2018) 25:35 57 DOI 10.3758/s13423-017-1343-3 Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications Eric-Jan Wagenmakers 1 Maarten Marsman 1 Tahira
More informationThe Frequentist Implications of Optional Stopping on Bayesian Hypothesis Tests
The Frequentist Implications of Optional Stopping on Bayesian Hypothesis Tests Adam N. Sanborn Thomas T. Hills Department of Psychology, University of Warwick Abstract Null hypothesis significance testing
More informationProfile Analysis. Intro and Assumptions Psy 524 Andrew Ainsworth
Profile Analysis Intro and Assumptions Psy 524 Andrew Ainsworth Profile Analysis Profile analysis is the repeated measures extension of MANOVA where a set of DVs are commensurate (on the same scale). Profile
More informationRegistered Reports - Guidelines for reviewers
Registered Reports - Guidelines for reviewers Registered Reports are a form of publication of empirical research in which the methods and proposed analyses are pre-registered and reviewed prior to the
More informationIntuitive Logic Revisited: New Data and a Bayesian Mixed Model Meta-Analysis
: New Data and a Bayesian Mixed Model Meta-Analysis Henrik Singmann*, Karl Christoph Klauer, David Kellen Institut für Psychologie, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany Abstract Recent
More informationSupplementary Materials: Materials and Methods Figures S1-S2 Tables S1-S17 References
Supplementary Materials: Materials and Methods Figures S1-S2 Tables S1-S17 References Materials and Methods Simon Task Participants were randomly assigned to one of four versions of the task. Upon return
More informationGeorge B. Ploubidis. The role of sensitivity analysis in the estimation of causal pathways from observational data. Improving health worldwide
George B. Ploubidis The role of sensitivity analysis in the estimation of causal pathways from observational data Improving health worldwide www.lshtm.ac.uk Outline Sensitivity analysis Causal Mediation
More information7 Statistical Issues that Researchers Shouldn t Worry (So Much) About
7 Statistical Issues that Researchers Shouldn t Worry (So Much) About By Karen Grace-Martin Founder & President About the Author Karen Grace-Martin is the founder and president of The Analysis Factor.
More informationImproving Inferences about Null Effects with Bayes Factors and Equivalence Tests
Improving Inferences about Null Effects with Bayes Factors and Equivalence Tests In Press, The Journals of Gerontology, Series B: Psychological Sciences Daniël Lakens, PhD Eindhoven University of Technology
More informationIntroduction to Multilevel Models for Longitudinal and Repeated Measures Data
Introduction to Multilevel Models for Longitudinal and Repeated Measures Data Today s Class: Features of longitudinal data Features of longitudinal models What can MLM do for you? What to expect in this
More informationEcological Statistics
A Primer of Ecological Statistics Second Edition Nicholas J. Gotelli University of Vermont Aaron M. Ellison Harvard Forest Sinauer Associates, Inc. Publishers Sunderland, Massachusetts U.S.A. Brief Contents
More informationCounty-Level Small Area Estimation using the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS)
County-Level Small Area Estimation using the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS) Van L. Parsons, Nathaniel Schenker Office of Research and
More informationIndividual Differences in Attention During Category Learning
Individual Differences in Attention During Category Learning Michael D. Lee (mdlee@uci.edu) Department of Cognitive Sciences, 35 Social Sciences Plaza A University of California, Irvine, CA 92697-5 USA
More informationINADEQUACIES OF SIGNIFICANCE TESTS IN
INADEQUACIES OF SIGNIFICANCE TESTS IN EDUCATIONAL RESEARCH M. S. Lalithamma Masoomeh Khosravi Tests of statistical significance are a common tool of quantitative research. The goal of these tests is to
More informationMS&E 226: Small Data
MS&E 226: Small Data Lecture 10: Introduction to inference (v2) Ramesh Johari ramesh.johari@stanford.edu 1 / 17 What is inference? 2 / 17 Where did our data come from? Recall our sample is: Y, the vector
More informationMidterm project due next Wednesday at 2 PM
Course Business Midterm project due next Wednesday at 2 PM Please submit on CourseWeb Next week s class: Discuss current use of mixed-effects models in the literature Short lecture on effect size & statistical
More informationST440/550: Applied Bayesian Statistics. (10) Frequentist Properties of Bayesian Methods
(10) Frequentist Properties of Bayesian Methods Calibrated Bayes So far we have discussed Bayesian methods as being separate from the frequentist approach However, in many cases methods with frequentist
More informationLec 02: Estimation & Hypothesis Testing in Animal Ecology
Lec 02: Estimation & Hypothesis Testing in Animal Ecology Parameter Estimation from Samples Samples We typically observe systems incompletely, i.e., we sample according to a designed protocol. We then
More informationEffect of Sample Size on Correlation and Regression Coefficients
Effect of Sample Size on Correlation and Regression Coefficients Swati Gupta 1 Research Scholar, Department of Education, Aligarh Muslim University, India Dr. Mamun Ali Naji Qasem 2 Faculty of Education,
More informationSPRING GROVE AREA SCHOOL DISTRICT. Course Description. Instructional Strategies, Learning Practices, Activities, and Experiences.
SPRING GROVE AREA SCHOOL DISTRICT PLANNED COURSE OVERVIEW Course Title: Basic Introductory Statistics Grade Level(s): 11-12 Units of Credit: 1 Classification: Elective Length of Course: 30 cycles Periods
More informationAppendix B. Contributed Work
Appendix B Contributed Work This appendix contains a list of the contributed publications and software packages until the completion of this PhD project. The PDF version of the dissertation contains links
More informationUndesirable Optimality Results in Multiple Testing? Charles Lewis Dorothy T. Thayer
Undesirable Optimality Results in Multiple Testing? Charles Lewis Dorothy T. Thayer 1 Intuitions about multiple testing: - Multiple tests should be more conservative than individual tests. - Controlling
More informationBayesian a posteriori performance estimation for speech recognition and psychophysical tasks
Bayesian a posteriori performance estimation page 1 of 6 Tantum, et al., Sumitted to JASA-EL 1/4/213 Bayesian a posteriori performance estimation for speech recognition and psychophysical tasks Stacy L.
More informationRISK AS AN EXPLANATORY FACTOR FOR RESEARCHERS INFERENTIAL INTERPRETATIONS
13th International Congress on Mathematical Education Hamburg, 24-31 July 2016 RISK AS AN EXPLANATORY FACTOR FOR RESEARCHERS INFERENTIAL INTERPRETATIONS Rink Hoekstra University of Groningen Logical reasoning
More informationBiostatistics II
Biostatistics II 514-5509 Course Description: Modern multivariable statistical analysis based on the concept of generalized linear models. Includes linear, logistic, and Poisson regression, survival analysis,
More informationInferential Statistics
Inferential Statistics and t - tests ScWk 242 Session 9 Slides Inferential Statistics Ø Inferential statistics are used to test hypotheses about the relationship between the independent and the dependent
More informationHierarchical Linear Models: Applications to cross-cultural comparisons of school culture
Hierarchical Linear Models: Applications to cross-cultural comparisons of school culture Magdalena M.C. Mok, Macquarie University & Teresa W.C. Ling, City Polytechnic of Hong Kong Paper presented at the
More informationThe role of sampling assumptions in generalization with multiple categories
The role of sampling assumptions in generalization with multiple categories Wai Keen Vong (waikeen.vong@adelaide.edu.au) Andrew T. Hendrickson (drew.hendrickson@adelaide.edu.au) Amy Perfors (amy.perfors@adelaide.edu.au)
More informationCHAPTER 1 RESEARCH QUESTIONS & HYPOTHESES & HYPOTHESES. Copyright 2014 Dr. Rich Schuttler AGENDA. Copyright 2014 Dr.
CHAPTER 1 RESEARCH QUESTIONS & HYPOTHESES & HYPOTHESES Introduction AGENDA 1 AGENDA Introduction Research Questions AGENDA Introduction Research Questions Hypotheses 2 AGENDA Introduction Research Questions
More informationDECISION ANALYSIS WITH BAYESIAN NETWORKS
RISK ASSESSMENT AND DECISION ANALYSIS WITH BAYESIAN NETWORKS NORMAN FENTON MARTIN NEIL CRC Press Taylor & Francis Croup Boca Raton London NewYork CRC Press is an imprint of the Taylor Si Francis an Croup,
More informationFour reasons to prefer Bayesian analyses over significance testing
Psychon Bull Rev (2018) 25:207 218 DOI 10.3758/s13423-017-1266-z Four reasons to prefer Bayesian analyses over significance testing Zoltan Dienes 1 & Neil Mclatchie 2 Published online: 28 March 2017 #
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) 1) A) B) C) D)
Exam Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) 1) A) B) C) D) Decide whether or not the conditions and assumptions for inference with
More informationHow many speakers? How many tokens?:
1 NWAV 38- Ottawa, Canada 23/10/09 How many speakers? How many tokens?: A methodological contribution to the study of variation. Jorge Aguilar-Sánchez University of Wisconsin-La Crosse 2 Sample size in
More informationCentering Predictors
Centering Predictors Longitudinal Data Analysis Workshop Section 3 University of Georgia: Institute for Interdisciplinary Research in Education and Human Development Section 3: Centering Covered this Section
More informationCommunication Research Practice Questions
Communication Research Practice Questions For each of the following questions, select the best answer from the given alternative choices. Additional instructions are given as necessary. Read each question
More informationBayesSDT: Software for Bayesian inference with signal detection theory
Behavior Research Methods 008, 40 (), 450-456 doi: 10.3758/BRM.40..450 BayesSDT: Software for Bayesian inference with signal detection theory MICHAEL D. LEE University of California, Irvine, California
More informationAnalysis of Variance (ANOVA)
Research Methods and Ethics in Psychology Week 4 Analysis of Variance (ANOVA) One Way Independent Groups ANOVA Brief revision of some important concepts To introduce the concept of familywise error rate.
More informationProblem Set 3 ECN Econometrics Professor Oscar Jorda. Name. ESSAY. Write your answer in the space provided.
Problem Set 3 ECN 140 - Econometrics Professor Oscar Jorda Name ESSAY. Write your answer in the space provided. 1) Sir Francis Galton, a cousin of James Darwin, examined the relationship between the height
More informationDetecting chance: A solution to the null sensitivity problem in subliminal priming
Psychonomic Bulletin & Review 7, 14 (4), 597-5 Detecting chance: A solution to the null sensitivity problem in subliminal priming JEFFREY N. ROUDER, RICHARD D. MOREY, PAUL L. SPECKMAN, AND MICHAEL S. PRATTE
More informationEvaluation of Cognitive Processing in Redundant Audio Visual Signals
Evaluation of Cognitive Processing in Redundant Audio Visual Signals Elizabeth L. Fox (fox.119@wright.edu) Joseph J. Glavan (glavan.3@wright.edu) Joseph W. Houpt (joseph.houpt@wright.edu) Wright State
More informationjoachim vandekerckhove October 1, 2018 curriculum vitae Contact information Employment history Education Professional memberships (past and present)
Contact information Department of Cognitive Sciences 2324 Social and Behavioral Sciences Gateway Building (SBSG) Irvine, CA 92697-5100 Phone: +1 (949) 824-5958 joachim@uci.edu http://www.cidlab.com/ Employment
More informationChapter 21 Multilevel Propensity Score Methods for Estimating Causal Effects: A Latent Class Modeling Strategy
Chapter 21 Multilevel Propensity Score Methods for Estimating Causal Effects: A Latent Class Modeling Strategy Jee-Seon Kim and Peter M. Steiner Abstract Despite their appeal, randomized experiments cannot
More informationLecture 21. RNA-seq: Advanced analysis
Lecture 21 RNA-seq: Advanced analysis Experimental design Introduction An experiment is a process or study that results in the collection of data. Statistical experiments are conducted in situations in
More informationKnowledge is Power: The Basics of SAS Proc Power
ABSTRACT Knowledge is Power: The Basics of SAS Proc Power Elaina Gates, California Polytechnic State University, San Luis Obispo There are many statistics applications where it is important to understand
More informationVALIDITY OF QUANTITATIVE RESEARCH
Validity 1 VALIDITY OF QUANTITATIVE RESEARCH Recall the basic aim of science is to explain natural phenomena. Such explanations are called theories (Kerlinger, 1986, p. 8). Theories have varying degrees
More informationRetrospective power analysis using external information 1. Andrew Gelman and John Carlin May 2011
Retrospective power analysis using external information 1 Andrew Gelman and John Carlin 2 11 May 2011 Power is important in choosing between alternative methods of analyzing data and in deciding on an
More informationIn this module I provide a few illustrations of options within lavaan for handling various situations.
In this module I provide a few illustrations of options within lavaan for handling various situations. An appropriate citation for this material is Yves Rosseel (2012). lavaan: An R Package for Structural
More informationf WILEY ANOVA and ANCOVA A GLM Approach Second Edition ANDREW RUTHERFORD Staffordshire, United Kingdom Keele University School of Psychology
ANOVA and ANCOVA A GLM Approach Second Edition ANDREW RUTHERFORD Keele University School of Psychology Staffordshire, United Kingdom f WILEY A JOHN WILEY & SONS, INC., PUBLICATION Contents Acknowledgments
More informationCOURSE/SUBJECT UNIT DESCRIPTION
COURSE/SUBJECT UNIT DESCRIPTION SUBJECT: PRINCIPLES OF KINESITHERAPY COURSE 1º SEMESTER. 2 DEGREE (S): PHYSIOTHERAPY TYPE OF COURSE: ATTENDANCE REQUIRED ACADEMIC YEAR 2015/2016 SCHOOL OF MEDICINE Course
More informationData and Statistics 101: Key Concepts in the Collection, Analysis, and Application of Child Welfare Data
TECHNICAL REPORT Data and Statistics 101: Key Concepts in the Collection, Analysis, and Application of Child Welfare Data CONTENTS Executive Summary...1 Introduction...2 Overview of Data Analysis Concepts...2
More informationCHAPTER-III METHODOLOGY
CHAPTER-III METHODOLOGY 3.1 INTRODUCTION This chapter deals with the methodology employed in order to achieve the set objectives of the study. Details regarding sample, description of the tools employed,
More informationJournal of Experimental Psychology: Learning, Memory, and Cognition
Journal of Experimental Psychology: Learning, Memory, and Cognition No Recovery of Memory When Cognitive Load Is Decreased Timothy J. Ricker, Evie Vergauwe, Garrett A. Hinrichs, Christopher L. Blume, and
More informationFinal Exam Version A
Final Exam Version A Open Book and Notes your 4-digit code: Staple the question sheets to your answers Write your name only once on the back of this sheet. Problem 1: (10 points) A popular method to isolate
More informationHow Does Analysis of Competing Hypotheses (ACH) Improve Intelligence Analysis?
How Does Analysis of Competing Hypotheses (ACH) Improve Intelligence Analysis? Richards J. Heuer, Jr. Version 1.2, October 16, 2005 This document is from a collection of works by Richards J. Heuer, Jr.
More information