Psychometrics an Introduction Second Edition
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An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
Arxiv:1908.07390V1 [Stat.AP] 19 Aug 2019
An Overview of Statistical Data Analysis Rui Portocarrero Sarmento Vera Costa LIAAD-INESC TEC FEUP, University of Porto PRODEI - FEUP, University of Porto [email protected] [email protected] August 21, 2019 Abstract The use of statistical software in academia and enterprises has been evolving over the last years. More often than not, students, professors, workers and users in general have all had, at some point, exposure to statistical software. Sometimes, difficulties are felt when dealing with such type of software. Very few persons have theoretical knowledge to clearly understand software configurations or settings, and sometimes even the presented results. Very often, the users are required by academies or enterprises to present reports, without the time to explore or understand the results or tasks required to do a optimal prepara- tion of data or software settings. In this work, we present a statistical overview of some theoretical concepts, to provide a fast access to some concepts. Keywords Reporting Mathematics Statistics and Applications · · 1 Introduction Statistics is a set of methods used to analyze data. The statistic is present in all areas of science involving the collection, handling and sorting of data, given the insight of a particular phenomenon and the possibility that, from that knowledge, inferring possible new results. One of the goals with statistics is to extract information from data to get a better understanding of the situations they represent. Thus, the statistics can be thought of as the science of learning from data. Currently, the high competitiveness in search technologies and markets has caused a constant race for the information. -
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 -
Discriminant Function Analysis
Discriminant Function Analysis Discriminant Function Analysis ● General Purpose ● Computational Approach ● Stepwise Discriminant Analysis ● Interpreting a Two-Group Discriminant Function ● Discriminant Functions for Multiple Groups ● Assumptions ● Classification General Purpose Discriminant function analysis is used to determine which variables discriminate between two or more naturally occurring groups. For example, an educational researcher may want to investigate which variables discriminate between high school graduates who decide (1) to go to college, (2) to attend a trade or professional school, or (3) to seek no further training or education. For that purpose the researcher could collect data on numerous variables prior to students' graduation. After graduation, most students will naturally fall into one of the three categories. Discriminant Analysis could then be used to determine which variable(s) are the best predictors of students' subsequent educational choice. A medical researcher may record different variables relating to patients' backgrounds in order to learn which variables best predict whether a patient is likely to recover completely (group 1), partially (group 2), or not at all (group 3). A biologist could record different characteristics of similar types (groups) of flowers, and then perform a discriminant function analysis to determine the set of characteristics that allows for the best discrimination between the types. To index Computational Approach Computationally, discriminant function analysis is very similar to analysis of variance (ANOVA ). Let us consider a simple example. Suppose we measure height in a random sample of 50 males and 50 females. Females are, on the average, not as tall as http://www.statsoft.com/textbook/stdiscan.html (1 of 11) [2/11/2008 8:56:30 AM] Discriminant Function Analysis males, and this difference will be reflected in the difference in means (for the variable Height ). -
Factor Analysis
Factor Analysis Qian-Li Xue Biostatistics Program Harvard Catalyst | The Harvard Clinical & Translational Science Center Short course, October 27, 2016 1 Well-used latent variable models Latent Observed variable scale variable scale Continuous Discrete Continuous Factor Discrete FA analysis IRT (item response) LISREL Discrete Latent profile Latent class Growth mixture analysis, regression General software: MPlus, Latent Gold, WinBugs (Bayesian), NLMIXED (SAS) Objectives § What is factor analysis? § What do we need factor analysis for? § What are the modeling assumptions? § How to specify, fit, and interpret factor models? § What is the difference between exploratory and confirmatory factor analysis? § What is and how to assess model identifiability? 3 What is factor analysis § Factor analysis is a theory driven statistical data reduction technique used to explain covariance among observed random variables in terms of fewer unobserved random variables named factors 4 An Example: General Intelligence (Charles Spearman, 1904) Y1 δ1 δ Y2 2 δ General Y3 3 Intelligence Y4 δ4 F Y5 δ5 5 Why Factor Analysis? 1. Testing of theory § Explain covariation among multiple observed variables by § Mapping variables to latent constructs (called “factors”) 2. Understanding the structure underlying a set of measures § Gain insight to dimensions § Construct validation (e.g., convergent validity) 3. Scale development § Exploit redundancy to improve scale’s validity and reliability 6 Part I. Exploratory Factor Analysis (EFA) 7 One Common Factor Model: Model Specification Y δ1 λ1 1 Y1 = λ1F +δ1 λ2 Y δ F 2 2 Y2 = λ2 F +δ 2 λ3 Y δ 3 3 Y3 = λ3F +δ3 § The factor F is not observed; only Y1, Y2, Y3 are observed § δi represent variability in the Yi NOT explained by F § Yi is a linear function of F and δi 8 One Common Factor Model: Model Assumptions λ Y δ1 1 1 Y1 = λ1F +δ1 λ2 Y δ F 2 2 Y2 = λ2 F +δ 2 λ3 Y δ 3 3 Y3 = λ3F +δ3 § Factorial causation § F is independent of δj, i.e. -
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 -
Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning
medRxiv preprint doi: https://doi.org/10.1101/2020.06.01.20119560; this version posted June 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning Serge Dolgikh1[0000-0001-5929-8954] Abstract. Based on a subset of Covid-19 Wave 1 cases at a time point near TZ+3m (April, 2020), we perform an analysis of the influencing factors for the epidemics impacts with several different statistical methods. The consistent con- clusion of the analysis with the available data is that apart from the policy and management quality, being the dominant factor, the most influential factors among the considered were current or recent universal BCG immunization and the prevalence of smoking. Keywords: Infectious epidemiology, Covid-19 1 Introduction A possible link between the effects of Covid-19 pandemics such as the rate of spread and the severity of cases; and a universal immunization program against tuberculosis with BCG vaccine (UIP, hereinafter) was suggested in [1] and further investigated in [2]. Here we provide a factor analysis based on the available data for the first group of countries that encountered the epidemics (Wave 1) by applying several commonly used factor-ranking methods. The intent of the work is to repeat similar analysis at several different points in the time series of cases that would allow to make a confident conclusion about the epide- miological and social factors with strong influence on the course of the epidemics. -
T-Test & Factor Analysis
University of Brighton Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives • Continue using parametric test if the sample is large or enough evidence available to prove the usage • Transforming and manipulating variables • Use non parametric test Dr. Paurav Shukla t-test Tests used for significant differences between Used when they are two groups only groups Compares the mean score on some continuous Independent sample t-test variable Paired sample t-test Largely used in measuring before – after effect One-way ANOVA (between groups) Also called intervention effect One-way ANOVA (repeated groups) Option 1: Same sample over a period of time Two-way ANOVA (between groups) Option 2: Two different groups at a single point of MANOVA time Assumptions associated with t-test Why t-test is important? Population distribution of sample is normal Highly used technique for hypothesis testing Probability (random) sampling Can lead to wrong conclusions Level of measurement Type 1 error Dependent variable is continuous • Reject null hypothesis instead of accepting – When we assume there is a difference between groups, when it Observed or measured data must be is not independent Type 2 error If interaction is unavoidable use a stringent alpha • Accept null hypothesis instead of rejecting value (p<.01) – When we assume there is no difference between groups, when Homogeneity of variance there is Assumes that samples are obtained from populations Solution of equal variance • Interdependency between both errors ANOVA is reasonably robust against this • Selection of alpha level Dr. -
Statistical Analysis in JASP
Copyright © 2018 by Mark A Goss-Sampson. All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author except for the purposes of research, education or private study. CONTENTS PREFACE .................................................................................................................................................. 1 USING THE JASP INTERFACE .................................................................................................................... 2 DESCRIPTIVE STATISTICS ......................................................................................................................... 8 EXPLORING DATA INTEGRITY ................................................................................................................ 15 ONE SAMPLE T-TEST ............................................................................................................................. 22 BINOMIAL TEST ..................................................................................................................................... 25 MULTINOMIAL TEST .............................................................................................................................. 28 CHI-SQUARE ‘GOODNESS-OF-FIT’ TEST............................................................................................. 30 MULTINOMIAL AND Χ2 ‘GOODNESS-OF-FIT’ TEST. .......................................................................... -
Properties and Psychometrics of a Measure of Addiction Recovery Strengths
bs_bs_banner REVIEW Drug and Alcohol Review (March 2013), 32, 187–194 DOI: 10.1111/j.1465-3362.2012.00489.x The Assessment of Recovery Capital: Properties and psychometrics of a measure of addiction recovery strengths TEODORA GROSHKOVA1, DAVID BEST2 & WILLIAM WHITE3 1National Addiction Centre, Institute of Psychiatry, King’s College London, London, UK, 2Turning Point Drug and Alcohol Centre, Monash University, Melbourne, Australia, and 3Chestnut Health Systems, Bloomington, Illinois, USA Abstract Introduction and Aims. Sociological work on social capital and its impact on health behaviours have been translated into the addiction field in the form of ‘recovery capital’ as the construct for assessing individual progress on a recovery journey.Yet there has been little attempt to quantify recovery capital.The aim of the project was to create a scale that assessed addiction recovery capital. Design and Methods. Initial focus group work identified and tested candidate items and domains followed by data collection from multiple sources to enable psychometric assessment of a scale measuring recovery capital. Results. The scale shows moderate test–retest reliability at 1 week and acceptable concurrent validity. Principal component analysis determined single factor structure. Discussion and Conclusions. The Assessment of Recovery Capital (ARC) is a brief and easy to administer measurement of recovery capital that has acceptable psychometric properties and may be a useful complement to deficit-based assessment and outcome monitoring instruments for substance dependent individuals in and out of treatment. [Groshkova T, Best D, White W. The Assessment of Recovery Capital: Properties and psychometrics of a measure of addiction recovery strengths. Drug Alcohol Rev 2013;32:187–194] Key words: addiction, recovery capital measure, assessment, psychometrics. -
Autocorrelation-Based Factor Analysis and Nonlinear Shrinkage Estimation of Large Integrated Covariance Matrix
Autocorrelation-based Factor Analysis and Nonlinear Shrinkage Estimation of Large Integrated covariance matrix Qilin Hu A Thesis Submitted for the Degree of Doctor of Philosophy Department of Statistics London School of Economics and Political Science Supervisors: Assoc Prof. Clifford Lam and Prof. Piotr Fryzlewicz London, Aug 2016 Declaration I certify that the thesis I have presented for examination for the MPhil/PhD degree of the London School of Economics and Political Science is solely my own work other than where I have clearly indicated that it is the work of others (in which case the extent of any work carried out jointly by me and any other person is clearly identified in it). The copyright of this thesis rests with the author. Quotation from it is permitted, provided that full acknowledgement is made. This thesis may not be reproduced without my prior written consent. I warrant that this authorisation does not, to the best of my belief, infringe the rights of any third party. I declare that my thesis consists of 4 chapters. Statement of conjoint work I confirm that chapter 3 was jointly co-authored with Assoc Prof. Clifford Lam. 2 Acknowledgements I would like to express my whole-hearted gratitude to my Ph.D. supervisor, Assoc Prof. Clifford Lam. For his tremendous support and encouragement during my study. I would also like to thank Prof. Piotr Fryzlewicz for his support. In addition, I want to express my appreciation to the Department of Statistics at LSE for providing me a fantastic research environment and partial financial support. Furthermore, I am grateful for the generosity of China Scholarship Council (CSC) who provides me financial support during the last three years. -
PSY610: PSYCHOMETRICS/STATISTICS FALL, 2006 Dr. Gregory T. Smith Teaching Assistant: 105 Kastle Hall Lauren Gudonis 257-6454 L
PSY610: PSYCHOMETRICS/STATISTICS FALL, 2006 Dr. Gregory T. Smith Teaching Assistant: 105 Kastle Hall Lauren Gudonis 257-6454 [email protected] [email protected] office: 111 J Office Hours: Monday and Wednesday 11:00 - 11:45 and by appt. Required Texts: 1. Keppel, G. (2004). Design and Analysis: A Researcher’s Handbook. (4th Edition). Upper Saddle River, N.J.: Pearson/Prentice-Hall, Inc. Course Outline 8/24 1. Introduction. Independent and dependent variables. Correlational and experimental studies. Implications for interpretation of results. Example. Randomization and Random Sampling. 8/29 2. Levels of Measurement. Examples. Relation of levels of measurement to statistical tests. Chart. Three major questions to be answered by statistical analysis: existence, nature, and magnitude of relationships between X and Y (or Y1 and Y2). Introduction to probability. 8/31 3. Probability: an intuitive approach and a formal approach. Independence and conditional probabilities. Introduction to the logic of hypothesis testing. 9/5 4. The logic of hypothesis testing. Introduction to parametric (normal distribution) techniques. One sample goodness of fit test z. Sampling distribution of the mean. Central limit theorem. Standard error of the mean. Lab #1 Introduction to lab. Probability. Definitions, examples, and exercises. Introduction to computer system. Homework #1 assigned. 9/7 5. Logic of hypothesis testing, decision rule, terms, power, Type I and Type II errors. 9/12 6. Student's t test: sampling distribution, procedures, assumptions, degrees of freedom. Unbiased and biased estimates. Directional v. nondirectional tests, confidence intervals, t and z compared. Lab #2 Review material from preceding two lectures; homework; SPSS exercises. 9/14 7.