Introduction to Mplus
|
|
- Lucy Fitzgerald
- 6 years ago
- Views:
Transcription
1 Introduction to Mplus May 12, 2010 SPONSORED BY: Research Data Centre Population and Life Course Studies PLCS Interdisciplinary Development Initiative Piotr Wilk
2 OVERVIEW Mplus modeling framework Mplus language Examples of research using Mplus
3 WHAT IS MPLUS? Statistical modeling program for structural equation modeling... and more Extremely flexible modeling framework: Multiple types of data formats (variables) Multiple types of statistical models (relationships) Code-based path-centric specification
4 MODELING FRAMEWORK
5 MODELING FRAMEWORK Describes structure of the (rectangles and circles) Describes relationships between variables (arrows) Acknowledges complex data structures: Multilevel and multiple population data
6 DATA STRUCTURE Observe variables (rectangles) Latent variables (circles) Combinations
7 OBSERVED OUTCOME VARIABLES Continuous (y) Categorical (u): Censored Binary Ordered categorical (ordinal) Unordered categorical (nominal) Counts Combinations: Single model (y with u)
8 LATANT VARIABLES Continuous latent variables (f): Continuous indicators Categorical indicators Categorical latent variables (c) Measurement model Group membership
9 RELATIONSHIPS: OBSERVED VARIABLES Linear regression for continuous outcomes Probit or logistic regression for binary outcomes Poisson or zero-inflated regression for count outcomes Simultaneous modeling of several related relationships: Path analysis
10 RELATIONSHIPS: LATANT VARIABLES Continuous latent variables: Structural equation modeling Categorical latent variables: Mixture modeling Latent class analysis Latent variable interactions
11 RELATIONSHIPS: ALL VARIABLES Ability to combine all types of variables and all types of relationships into a single analytical framework
12 MODELING POSSIBILITIES: EXAMPLES Complex survey data Multiple group analysis Multilevel modeling Mixture modeling Latent class analysis Longitudinal data analysis Modeling with missing data Monte Carlo simulations And more
13 COMPLEX SURVEY DATA Adjustment of standard errors: Takes into account stratification and/or nonindependence of observations Unequal probabilities of selection (sampling weights) Multilevel framework: Specify a separate model for each level of the multilevel data Both approaches can be combined
14 MULTILEVEL MODELING Multilevel models separate the overall variance into two sources: Within (individual-level variation) Between (group-level variation) Allows random intercepts and random slopes Random effects can be specified for any relationship
15 MIXTURE MODELING Modeling with categorical latent variables Represent subpopulations where population membership not known but inferred from the data
16 LATENT CLASS ANALYSIS A special case of mixture modeling Explains relationships among observed dependent variables Provides classification of individuals into more homogenous sub-groups
17 LONGITUDINAL DATA ANALYSIS Broad class of statistical methods for longitudinal data Latent growth curve analysis Resembles classic confirmatory factor analysis Multilevel modeling
18 MODELING WITH MISSING DATA Several options for estimating models with missing data Estimation based on two assumptions: Missing completely at random Missing at random Non-ignorable missing data modeling: Categorical outcomes as indicators of missingness Generates and analyzes multiple data sets using multiple imputation Computes bootstrapped standard errors
19 MONTE CARLO SIMULATIONS Extensive Monte Carlo facilities for data generation and data analysis Generates several types of data based on specified parameters Can be used for power analysis Other Monte Carlo features: Saving generated data and parameter estimates Analytical results from each replication can be saved in an external file
20 OTHER USEFUL FEATURES Indirect effects (specific paths) Bootstrap standard errors and confidence intervals Robust estimation of standard errors and chi-square tests for model fit And more
21 COMMAND STRUCTURE Mplus is a command-based program There are nine sets of Mplus commands: TITLE: DATA: VARIABLE: DEFINE: ANALYSIS: MODEL: OUTPUT: SAVEDATA: PLOT: MONTECARLO:
22 GENERAL RULES All commands must begin on a new line and must be followed by a colon (:) Some commands have numerous subcommands Semicolons (;) separate subcommands Individual lines of code cannot exceed 80 characters Not case sensitive (only variable names are case sensitive) Exclamation mark in front (!) serves as a comment character
23 TITLE COMMAND Specifies a title that will be printed on each page of the output file No limit on length
24 DATA COMMAND Specifies where the data file is located and the format of the data Records may be in free format or fixed format Accepts covariance or correlation matrices Data files from other statistical packages have to be converted: SAS and SPSS: fixed format ASCII file STATA: stata2mplus function
25 DEFINE COMMAND Allows for transformation and creation of new variables Supports a large number of transformation functions Allows for conditional statements Selection of observations
26 ANALYSIS COMMAND Specifies analysis type(s) and estimation procedure Many estimation options are available Some analyses require additional commands
27 MODEL COMMAND: OVERVIEW Specifies the parameters of the model Models are built in terms of relationships between variables: Variable RELATIONSHIP Variable
28 MODEL COMMAND: RELATIONSHIPS BY keyword ("measured by"): Define the latent variables ON keyword ( regressed on ): Structural path between variables WITH keyword ( correlated with ): correlation between two variables
29 Variances: MODEL COMMAND: PARAMETERS Variable name without brackets [Means] or thresholds [catvar$1]: Variable name inside square brackets {Scale factors}: Variable name in curly brackets
30 OUTPUT COMMAND Specifies optional outputs to be generated Mplus creates an output file using the extension.out (text file) Specific elements of output can be included or suppressed
31 SAVEDATA COMMAND Determines what to save in new text files Analysis dependent outputs Datasets Parameter estimates Latent class memberships Cook s distances or influence statistics
32 PLOT COMMAND Provides graphical displays of observed data and results: Histograms / scatterplots Individual observed and estimated values Sample and estimated means and proportions/probabilities Available for: Total sample By group / class Adjusted for covariates Editing and exporting of plots
33 DEFAULTS The command language is set up with defaults to minimize the amount of text Version specific defaults Example: Missing data Mplus assumes that there are no missing values or that FIML estimation (missing values are missing at random) Listwise deletion must be specified under the DATA command
34 SUMMARY: PROS Many great features not available in other packages Ability to combine various data types Path-centric specification: Relatively intuitive and easy to learn Extensions to larger models are easy to implement Commitment to development Excellent support
35 SUMMARY: CONS Cost: Mplus is a commercial package Annual fee: Support and updates Matrix specification is not supported No data management beyond Monte Carlo capabilities, transformations, and selection of observations
36 ADDITIONAL RESOURCES Technical and theoretical support: Homepage: Discussion forum: Online manuals and tutorials Other websites:
37 MPLUS COMMERCIAL VERSION Current version: 6.0 (new!) Base Program: 595 USD Mixture "add-on": 745 USD Multilevel "add-on": 745 USD Combination "add-on": 895 USD
38 MPLUS DEMO VERSION Free version of the software Limit on the number of variables 2 independent variables 6 dependent variables
39 CONCLUSION Advantages and disadvantage of using only one program Each program has strengths and weaknesses Use the correct one for the problem at hand
40 EXAMPLES Path analysis (3.11) Structural equation modeling (511) Latent growth curve analysis Quadratic growth (6.9) Paralleled processes (6.13) Mixture modeling (7.1) Advanced models Latent class growth curves analysis Complier average causal effect
41 PATH ANALYSIS
42 PATH ANALYSIS TITLE: Path analysis with continuous dependent variables DATA: FILE IS ex3.11.dat; VARIABLE: NAMES ARE y1-y3 x1-x3; MODEL: y1 y2 ON x1 x2 x3; y3 ON y1 y2 x2;
43 STRUCTURAL EQUATION MODEL
44 STRUCTURAL EQUATION MODEL TITLE: SEM with continuous indicators DATA: FILE IS ex5.11.dat; VARIABLE: NAMES ARE y1-y12; MODEL: f1 BY y1-y3; f2 BY y4-y6; f3 BY y7-y9; f4 BY y10-y12; f4 ON f3; f3 ON f1 f2;
45 LATENT GROWTH MODEL
46 LATENT GROWTH MODEL TITLE: Quadratic growth model DATA: FILE IS ex6.9.dat; VARIABLE: NAMES ARE y11-y14; MODEL: i s q y11@0 y12@1 y13@2 y14@3; PLOT: Type is Plot3; Series = y11 (0) y12 (1) y13 (2) y14 (3);
47 LATENT GROWTH MODEL
48 LATENT GROWTH MODEL TITLE: Growth model for two parallel processes DATA: FILE IS ex6.13.dat; VARIABLE: NAMES ARE y11- y24; MODEL: i1 s1 i2 s2 s1 ON i2; s2 ON i1;
49 MIXTURE MODEL
50 MIXTURE MODEL TITLE: Mixture regression analysis DATA: FILE IS ex7.1.dat; VARIABLE: NAMES ARE y x1 x2; CLASSES = c (2); ANALYSIS: TYPE = MIXTURE; MODEL: %OVERALL% y ON x1 x2; c ON x1; %c#2% y ON x2; y;
51 GROWTH MIXTURE MODEL
52 COMPLIER AVERAGE CAUSAL EFFECT Outcome Compliance Covariates Treatment
CHAPTER 1 INTRODUCTION
Introduction CHAPTER 1 INTRODUCTION Mplus is a statistical modeling program that provides researchers with a flexible tool to analyze their data. Mplus offers researchers a wide choice of models, estimators,
More informationCHAPTER 7 EXAMPLES: MIXTURE MODELING WITH CROSS- SECTIONAL DATA
Examples: Mixture Modeling With Cross-Sectional Data CHAPTER 7 EXAMPLES: MIXTURE MODELING WITH CROSS- SECTIONAL DATA Mixture modeling refers to modeling with categorical latent variables that represent
More informationCHAPTER 11 EXAMPLES: MISSING DATA MODELING AND BAYESIAN ANALYSIS
Examples: Missing Data Modeling And Bayesian Analysis CHAPTER 11 EXAMPLES: MISSING DATA MODELING AND BAYESIAN ANALYSIS Mplus provides estimation of models with missing data using both frequentist and Bayesian
More informationPSY 9556B (Jan8) Design Issues and Missing Data Continued Examples of Simulations for Projects
PSY 9556B (Jan8) Design Issues and Missing Data Continued Examples of Simulations for Projects Let s create a data for a variable measured repeatedly over five occasions We could create raw data (for each
More informationAcknowledgments. Acronyms
Acknowledgments Preface Acronyms xi xiii xv 1 Basic Tools 1 1.1 Goals of inference 1 1.1.1 Population or process? 1 1.1.2 Probability samples 2 1.1.3 Sampling weights 3 1.1.4 Design effects. 5 1.2 An introduction
More informationGeneralized least squares (GLS) estimates of the level-2 coefficients,
Contents 1 Conceptual and Statistical Background for Two-Level Models...7 1.1 The general two-level model... 7 1.1.1 Level-1 model... 8 1.1.2 Level-2 model... 8 1.2 Parameter estimation... 9 1.3 Empirical
More informationA Beginner's Guide to. Randall E. Schumacker. The University of Alabama. Richard G. Lomax. The Ohio State University. Routledge
A Beginner's Guide to Randall E. Schumacker The University of Alabama Richard G. Lomax The Ohio State University Routledge Taylor & Francis Group New York London About the Authors Preface xv xvii 1 Introduction
More informationUsing Mplus Monte Carlo Simulations In Practice: A Note On Non-Normal Missing Data In Latent Variable Models
Using Mplus Monte Carlo Simulations In Practice: A Note On Non-Normal Missing Data In Latent Variable Models Bengt Muth en University of California, Los Angeles Tihomir Asparouhov Muth en & Muth en Mplus
More informationCHAPTER 13 EXAMPLES: SPECIAL FEATURES
Examples: Special Features CHAPTER 13 EXAMPLES: SPECIAL FEATURES In this chapter, special features not illustrated in the previous example chapters are discussed. A cross-reference to the original example
More informationCHAPTER 18 OUTPUT, SAVEDATA, AND PLOT COMMANDS
OUTPUT, SAVEDATA, And PLOT Commands CHAPTER 18 OUTPUT, SAVEDATA, AND PLOT COMMANDS THE OUTPUT COMMAND OUTPUT: In this chapter, the OUTPUT, SAVEDATA, and PLOT commands are discussed. The OUTPUT command
More informationCorrectly Compute Complex Samples Statistics
SPSS Complex Samples 15.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample
More informationStudy Guide. Module 1. Key Terms
Study Guide Module 1 Key Terms general linear model dummy variable multiple regression model ANOVA model ANCOVA model confounding variable squared multiple correlation adjusted squared multiple correlation
More informationThe Mplus modelling framework
The Mplus modelling framework Continuous variables Categorical variables 1 Mplus syntax structure TITLE: a title for the analysis (not part of the syntax) DATA: (required) information about the data set
More informationHandbook of Statistical Modeling for the Social and Behavioral Sciences
Handbook of Statistical Modeling for the Social and Behavioral Sciences Edited by Gerhard Arminger Bergische Universität Wuppertal Wuppertal, Germany Clifford С. Clogg Late of Pennsylvania State University
More informationPSY 9556B (Feb 5) Latent Growth Modeling
PSY 9556B (Feb 5) Latent Growth Modeling Fixed and random word confusion Simplest LGM knowing how to calculate dfs How many time points needed? Power, sample size Nonlinear growth quadratic Nonlinear growth
More informationCorrectly Compute Complex Samples Statistics
PASW Complex Samples 17.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample
More informationAn Introduction to Growth Curve Analysis using Structural Equation Modeling
An Introduction to Growth Curve Analysis using Structural Equation Modeling James Jaccard New York University 1 Overview Will introduce the basics of growth curve analysis (GCA) and the fundamental questions
More information3.6 Sample code: yrbs_data <- read.spss("yrbs07.sav",to.data.frame=true)
InJanuary2009,CDCproducedareportSoftwareforAnalyisofYRBSdata, describingtheuseofsas,sudaan,stata,spss,andepiinfoforanalyzingdatafrom theyouthriskbehaviorssurvey. ThisreportprovidesthesameinformationforRandthesurveypackage.Thetextof
More informationMonte Carlo 1. Appendix A
Monte Carlo 1 Appendix A MONTECARLO:! A Monte Carlo study ensues. NAMES = g1 g2 g3 g4 p1 p2 p3 m1 m2 m3 m4 t1 t2 t3 t4 c1 c2 c3;! Desired name of each variable in the generated data. NOBS = 799;! Desired
More informationANNOUNCING THE RELEASE OF LISREL VERSION BACKGROUND 2 COMBINING LISREL AND PRELIS FUNCTIONALITY 2 FIML FOR ORDINAL AND CONTINUOUS VARIABLES 3
ANNOUNCING THE RELEASE OF LISREL VERSION 9.1 2 BACKGROUND 2 COMBINING LISREL AND PRELIS FUNCTIONALITY 2 FIML FOR ORDINAL AND CONTINUOUS VARIABLES 3 THREE-LEVEL MULTILEVEL GENERALIZED LINEAR MODELS 3 FOUR
More informationMPLUS Analysis Examples Replication Chapter 10
MPLUS Analysis Examples Replication Chapter 10 Mplus includes all input code and output in the *.out file. This document contains selected output from each analysis for Chapter 10. All data preparation
More informationRonald H. Heck 1 EDEP 606 (F2015): Multivariate Methods rev. November 16, 2015 The University of Hawai i at Mānoa
Ronald H. Heck 1 In this handout, we will address a number of issues regarding missing data. It is often the case that the weakest point of a study is the quality of the data that can be brought to bear
More informationJMP Book Descriptions
JMP Book Descriptions The collection of JMP documentation is available in the JMP Help > Books menu. This document describes each title to help you decide which book to explore. Each book title is linked
More informationCHAPTER 5. BASIC STEPS FOR MODEL DEVELOPMENT
CHAPTER 5. BASIC STEPS FOR MODEL DEVELOPMENT This chapter provides step by step instructions on how to define and estimate each of the three types of LC models (Cluster, DFactor or Regression) and also
More informationAnalysis of Complex Survey Data with SAS
ABSTRACT Analysis of Complex Survey Data with SAS Christine R. Wells, Ph.D., UCLA, Los Angeles, CA The differences between data collected via a complex sampling design and data collected via other methods
More informationMissing Data Missing Data Methods in ML Multiple Imputation
Missing Data Missing Data Methods in ML Multiple Imputation PRE 905: Multivariate Analysis Lecture 11: April 22, 2014 PRE 905: Lecture 11 Missing Data Methods Today s Lecture The basics of missing data:
More informationBinary IFA-IRT Models in Mplus version 7.11
Binary IFA-IRT Models in Mplus version 7.11 Example data: 635 older adults (age 80-100) self-reporting on 7 items assessing the Instrumental Activities of Daily Living (IADL) as follows: 1. Housework (cleaning
More informationSTATISTICS (STAT) Statistics (STAT) 1
Statistics (STAT) 1 STATISTICS (STAT) STAT 2013 Elementary Statistics (A) Prerequisites: MATH 1483 or MATH 1513, each with a grade of "C" or better; or an acceptable placement score (see placement.okstate.edu).
More informationSupplementary Notes on Multiple Imputation. Stephen du Toit and Gerhard Mels Scientific Software International
Supplementary Notes on Multiple Imputation. Stephen du Toit and Gerhard Mels Scientific Software International Part A: Comparison with FIML in the case of normal data. Stephen du Toit Multivariate data
More informationAbout Me. Mplus: A Tutorial. Today. Contacting Me. Today. Today. Hands-on training. Intermediate functions
About Me Mplus: A Tutorial Abby L. Braitman, Ph.D. Old Dominion University November 7, 2014 NOTE: Multigroup Analysis code was updated May 3, 2016 B.A. from UMD Briefly at NYU Ph.D. from ODU in 2012 (AE)
More informationUSER S GUIDE LATENT GOLD 4.0. Innovations. Statistical. Jeroen K. Vermunt & Jay Magidson. Thinking outside the brackets! TM
LATENT GOLD 4.0 USER S GUIDE Jeroen K. Vermunt & Jay Magidson Statistical Innovations Thinking outside the brackets! TM For more information about Statistical Innovations Inc. please visit our website
More informationMplus: A Tutorial. About Me
Mplus: A Tutorial Abby L. Braitman, Ph.D. Old Dominion University November 7, 2014 NOTE: Multigroup Analysis code was updated May 3, 2016 1 About Me B.A. from UMD Briefly at NYU Ph.D. from ODU in 2012
More informationPerformance of Latent Growth Curve Models with Binary Variables
Performance of Latent Growth Curve Models with Binary Variables Jason T. Newsom & Nicholas A. Smith Department of Psychology Portland State University 1 Goal Examine estimation of latent growth curve models
More informationLISREL 10.1 RELEASE NOTES 2 1 BACKGROUND 2 2 MULTIPLE GROUP ANALYSES USING A SINGLE DATA FILE 2
LISREL 10.1 RELEASE NOTES 2 1 BACKGROUND 2 2 MULTIPLE GROUP ANALYSES USING A SINGLE DATA FILE 2 3 MODELS FOR GROUPED- AND DISCRETE-TIME SURVIVAL DATA 5 4 MODELS FOR ORDINAL OUTCOMES AND THE PROPORTIONAL
More informationPRI Workshop Introduction to AMOS
PRI Workshop Introduction to AMOS Krissy Zeiser Pennsylvania State University klz24@pop.psu.edu 2-pm /3/2008 Setting up the Dataset Missing values should be recoded in another program (preferably with
More information**************************************************************************************************************** * Single Wave Analyses.
ASDA2 ANALYSIS EXAMPLE REPLICATION SPSS C11 * Syntax for Analysis Example Replication C11 * Use data sets previously prepared in SAS, refer to SAS Analysis Example Replication for C11 for details. ****************************************************************************************************************
More informationCLAREMONT MCKENNA COLLEGE. Fletcher Jones Student Peer to Peer Technology Training Program. Basic Statistics using Stata
CLAREMONT MCKENNA COLLEGE Fletcher Jones Student Peer to Peer Technology Training Program Basic Statistics using Stata An Introduction to Stata A Comparison of Statistical Packages... 3 Opening Stata...
More informationMissing Data Techniques
Missing Data Techniques Paul Philippe Pare Department of Sociology, UWO Centre for Population, Aging, and Health, UWO London Criminometrics (www.crimino.biz) 1 Introduction Missing data is a common problem
More informationMean Tests & X 2 Parametric vs Nonparametric Errors Selection of a Statistical Test SW242
Mean Tests & X 2 Parametric vs Nonparametric Errors Selection of a Statistical Test SW242 Creation & Description of a Data Set * 4 Levels of Measurement * Nominal, ordinal, interval, ratio * Variable Types
More informationSAS Structural Equation Modeling 1.3 for JMP
SAS Structural Equation Modeling 1.3 for JMP SAS Documentation The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2012. SAS Structural Equation Modeling 1.3 for JMP. Cary,
More informationMinitab 18 Feature List
Minitab 18 Feature List * New or Improved Assistant Measurement systems analysis * Capability analysis Graphical analysis Hypothesis tests Regression DOE Control charts * Graphics Scatterplots, matrix
More informationLudwig Fahrmeir Gerhard Tute. Statistical odelling Based on Generalized Linear Model. íecond Edition. . Springer
Ludwig Fahrmeir Gerhard Tute Statistical odelling Based on Generalized Linear Model íecond Edition. Springer Preface to the Second Edition Preface to the First Edition List of Examples List of Figures
More informationAn introduction to SPSS
An introduction to SPSS To open the SPSS software using U of Iowa Virtual Desktop... Go to https://virtualdesktop.uiowa.edu and choose SPSS 24. Contents NOTE: Save data files in a drive that is accessible
More informationSTATISTICS (STAT) 200 Level Courses. 300 Level Courses. Statistics (STAT) 1
Statistics (STAT) 1 STATISTICS (STAT) 200 Level Courses STAT 250: Introductory Statistics I. 3 credits. Elementary introduction to statistics. Topics include descriptive statistics, probability, and estimation
More informationSPSS Modules Features
SPSS Modules Features Core System Functionality (included in every license) Data access and management Data Prep features: Define Variable properties tool; copy data properties tool, Visual Bander, Identify
More informationSTATISTICS (STAT) 200 Level Courses Registration Restrictions: STAT 250: Required Prerequisites: not Schedule Type: Mason Core: STAT 346:
Statistics (STAT) 1 STATISTICS (STAT) 200 Level Courses STAT 250: Introductory Statistics I. 3 credits. Elementary introduction to statistics. Topics include descriptive statistics, probability, and estimation
More informationPredict Outcomes and Reveal Relationships in Categorical Data
PASW Categories 18 Specifications Predict Outcomes and Reveal Relationships in Categorical Data Unleash the full potential of your data through predictive analysis, statistical learning, perceptual mapping,
More informationbook 2014/5/6 15:21 page v #3 List of figures List of tables Preface to the second edition Preface to the first edition
book 2014/5/6 15:21 page v #3 Contents List of figures List of tables Preface to the second edition Preface to the first edition xvii xix xxi xxiii 1 Data input and output 1 1.1 Input........................................
More informationTechnical Support Minitab Version Student Free technical support for eligible products
Technical Support Free technical support for eligible products All registered users (including students) All registered users (including students) Registered instructors Not eligible Worksheet Size Number
More informationMinitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D.
Minitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D. Introduction to Minitab The interface for Minitab is very user-friendly, with a spreadsheet orientation. When you first launch Minitab, you will see
More informationMissing Data and Imputation
Missing Data and Imputation NINA ORWITZ OCTOBER 30 TH, 2017 Outline Types of missing data Simple methods for dealing with missing data Single and multiple imputation R example Missing data is a complex
More informationMINITAB Release Comparison Chart Release 14, Release 13, and Student Versions
Technical Support Free technical support Worksheet Size All registered users, including students Registered instructors Number of worksheets Limited only by system resources 5 5 Number of cells per worksheet
More informationLatent Curve Models. A Structural Equation Perspective WILEY- INTERSCIENŒ KENNETH A. BOLLEN
Latent Curve Models A Structural Equation Perspective KENNETH A. BOLLEN University of North Carolina Department of Sociology Chapel Hill, North Carolina PATRICK J. CURRAN University of North Carolina Department
More informationJMP 10 Student Edition Quick Guide
JMP 10 Student Edition Quick Guide Instructions presume an open data table, default preference settings and appropriately typed, user-specified variables of interest. RMC = Click Right Mouse Button Graphing
More informationScatterplot: The Bridge from Correlation to Regression
Scatterplot: The Bridge from Correlation to Regression We have already seen how a histogram is a useful technique for graphing the distribution of one variable. Here is the histogram depicting the distribution
More informationBlimp User s Guide. Version 1.0. Brian T. Keller. Craig K. Enders.
Blimp User s Guide Version 1.0 Brian T. Keller bkeller2@ucla.edu Craig K. Enders cenders@psych.ucla.edu September 2017 Developed by Craig K. Enders and Brian T. Keller. Blimp was developed with funding
More informationStatistical Methods for the Analysis of Repeated Measurements
Charles S. Davis Statistical Methods for the Analysis of Repeated Measurements With 20 Illustrations #j Springer Contents Preface List of Tables List of Figures v xv xxiii 1 Introduction 1 1.1 Repeated
More informationMultiple Imputation for Multilevel Models with Missing Data Using Stat-JR
Multiple Imputation for Multilevel Models with Missing Data Using Stat-JR Introduction In this document we introduce a Stat-JR super-template for 2-level data that allows for missing values in explanatory
More informationMissing Data Analysis for the Employee Dataset
Missing Data Analysis for the Employee Dataset 67% of the observations have missing values! Modeling Setup Random Variables: Y i =(Y i1,...,y ip ) 0 =(Y i,obs, Y i,miss ) 0 R i =(R i1,...,r ip ) 0 ( 1
More informationMultiple Imputation for Missing Data. Benjamin Cooper, MPH Public Health Data & Training Center Institute for Public Health
Multiple Imputation for Missing Data Benjamin Cooper, MPH Public Health Data & Training Center Institute for Public Health Outline Missing data mechanisms What is Multiple Imputation? Software Options
More informationDealing with Categorical Data Types in a Designed Experiment
Dealing with Categorical Data Types in a Designed Experiment Part II: Sizing a Designed Experiment When Using a Binary Response Best Practice Authored by: Francisco Ortiz, PhD STAT T&E COE The goal of
More informationStatistical Matching using Fractional Imputation
Statistical Matching using Fractional Imputation Jae-Kwang Kim 1 Iowa State University 1 Joint work with Emily Berg and Taesung Park 1 Introduction 2 Classical Approaches 3 Proposed method 4 Application:
More informationLearn What s New. Statistical Software
Statistical Software Learn What s New Upgrade now to access new and improved statistical features and other enhancements that make it even easier to analyze your data. The Assistant Data Customization
More informationAlso, for all analyses, two other files are produced upon program completion.
MIXOR for Windows Overview MIXOR is a program that provides estimates for mixed-effects ordinal (and binary) regression models. This model can be used for analysis of clustered or longitudinal (i.e., 2-level)
More informationInstructions for Using ABCalc James Alan Fox Northeastern University Updated: August 2009
Instructions for Using ABCalc James Alan Fox Northeastern University Updated: August 2009 Thank you for using ABCalc, a statistical calculator to accompany several introductory statistics texts published
More informationQuick Start Guide Jacob Stolk PhD Simone Stolk MPH November 2018
Quick Start Guide Jacob Stolk PhD Simone Stolk MPH November 2018 Contents Introduction... 1 Start DIONE... 2 Load Data... 3 Missing Values... 5 Explore Data... 6 One Variable... 6 Two Variables... 7 All
More informationIBM SPSS Statistics Traditional License packages and features
IBM SPSS Statistics Traditional License packages and features 1 2 3 The includes the following features: Data access and management Compare two data files for compatibility Data prep features: Define Variable
More informationSAS High-Performance Analytics Products
Fact Sheet What do SAS High-Performance Analytics products do? With high-performance analytics products from SAS, you can develop and process models that use huge amounts of diverse data. These products
More informationExcel 2010 with XLSTAT
Excel 2010 with XLSTAT J E N N I F E R LE W I S PR I E S T L E Y, PH.D. Introduction to Excel 2010 with XLSTAT The layout for Excel 2010 is slightly different from the layout for Excel 2007. However, with
More informationREALCOM-IMPUTE: multiple imputation using MLwin. Modified September Harvey Goldstein, Centre for Multilevel Modelling, University of Bristol
REALCOM-IMPUTE: multiple imputation using MLwin. Modified September 2014 by Harvey Goldstein, Centre for Multilevel Modelling, University of Bristol This description is divided into two sections. In the
More informationResources for statistical assistance. Quantitative covariates and regression analysis. Methods for predicting continuous outcomes.
Resources for statistical assistance Quantitative covariates and regression analysis Carolyn Taylor Applied Statistics and Data Science Group (ASDa) Department of Statistics, UBC January 24, 2017 Department
More informationSupplementary Material. 4) Mplus input code to estimate the latent transition analysis model
WORKING CONDITIONS AMONG HIGH-SKILLED WORKERS S1 Supplementary Material 1) Representativeness of the analytic sample 2) Cross-sectional latent class analyses 3) Mplus input code to estimate the latent
More informationRandom Number Generation and Monte Carlo Methods
James E. Gentle Random Number Generation and Monte Carlo Methods With 30 Illustrations Springer Contents Preface vii 1 Simulating Random Numbers from a Uniform Distribution 1 1.1 Linear Congruential Generators
More informationLast updated January 4, 2012
Last updated January 4, 2012 This document provides a description of Mplus code for implementing mixture factor analysis with four latent class components with and without covariates described in the following
More informationBig Data Methods. Chapter 5: Machine learning. Big Data Methods, Chapter 5, Slide 1
Big Data Methods Chapter 5: Machine learning Big Data Methods, Chapter 5, Slide 1 5.1 Introduction to machine learning What is machine learning? Concerned with the study and development of algorithms that
More informationEstimating DCMs Using Mplus. Chapter 9 Example Data
Estimating DCMs Using Mplus 1 NCME 2012: Diagnostic Measurement Workshop Chapter 9 Example Data Example assessment 7 items Measuring 3 attributes Q matrix Item Attribute 1 Attribute 2 Attribute 3 1 1 0
More informationCHAPTER 16 ANALYSIS COMMAND
ANALYSIS Command CHAPTER 16 ANALYSIS COMMAND THE ANALYSIS COMMAND ANALYSIS: In this chapter, the ANALYSIS command is discussed. The ANALYSIS command is used to describe the technical details of the analysis
More informationUsing Amos For Structural Equation Modeling In Market Research
Using Amos For Structural Equation Modeling In Market Research We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your
More informationFurther Maths Notes. Common Mistakes. Read the bold words in the exam! Always check data entry. Write equations in terms of variables
Further Maths Notes Common Mistakes Read the bold words in the exam! Always check data entry Remember to interpret data with the multipliers specified (e.g. in thousands) Write equations in terms of variables
More informationDesign of Experiments
Seite 1 von 1 Design of Experiments Module Overview In this module, you learn how to create design matrices, screen factors, and perform regression analysis and Monte Carlo simulation using Mathcad. Objectives
More informationEstimation of Item Response Models
Estimation of Item Response Models Lecture #5 ICPSR Item Response Theory Workshop Lecture #5: 1of 39 The Big Picture of Estimation ESTIMATOR = Maximum Likelihood; Mplus Any questions? answers Lecture #5:
More informationIntroduction. About this Document. What is SPSS. ohow to get SPSS. oopening Data
Introduction About this Document This manual was written by members of the Statistical Consulting Program as an introduction to SPSS 12.0. It is designed to assist new users in familiarizing themselves
More informationData analysis using Microsoft Excel
Introduction to Statistics Statistics may be defined as the science of collection, organization presentation analysis and interpretation of numerical data from the logical analysis. 1.Collection of Data
More informationSTA 570 Spring Lecture 5 Tuesday, Feb 1
STA 570 Spring 2011 Lecture 5 Tuesday, Feb 1 Descriptive Statistics Summarizing Univariate Data o Standard Deviation, Empirical Rule, IQR o Boxplots Summarizing Bivariate Data o Contingency Tables o Row
More informationSOS3003 Applied data analysis for social science Lecture note Erling Berge Department of sociology and political science NTNU.
SOS3003 Applied data analysis for social science Lecture note 04-2009 Erling Berge Department of sociology and political science NTNU Erling Berge 2009 1 Missing data Literature Allison, Paul D 2002 Missing
More information1. Estimation equations for strip transect sampling, using notation consistent with that used to
Web-based Supplementary Materials for Line Transect Methods for Plant Surveys by S.T. Buckland, D.L. Borchers, A. Johnston, P.A. Henrys and T.A. Marques Web Appendix A. Introduction In this on-line appendix,
More informationHierarchical Generalized Linear Models
Generalized Multilevel Linear Models Introduction to Multilevel Models Workshop University of Georgia: Institute for Interdisciplinary Research in Education and Human Development 07 Generalized Multilevel
More informationPart I, Chapters 4 & 5. Data Tables and Data Analysis Statistics and Figures
Part I, Chapters 4 & 5 Data Tables and Data Analysis Statistics and Figures Descriptive Statistics 1 Are data points clumped? (order variable / exp. variable) Concentrated around one value? Concentrated
More informationBasic Medical Statistics Course
Basic Medical Statistics Course S0 SPSS Intro November 2013 Wilma Heemsbergen w.heemsbergen@nki.nl 1 13.00 ~ 15.30 Database (20 min) SPSS (40 min) Short break Exercise (60 min) This Afternoon During the
More informationAnalytical model A structure and process for analyzing a dataset. For example, a decision tree is a model for the classification of a dataset.
Glossary of data mining terms: Accuracy Accuracy is an important factor in assessing the success of data mining. When applied to data, accuracy refers to the rate of correct values in the data. When applied
More informationIntroduction to Hierarchical Linear Model. Hsueh-Sheng Wu CFDR Workshop Series January 30, 2017
Introduction to Hierarchical Linear Model Hsueh-Sheng Wu CFDR Workshop Series January 30, 2017 1 Outline What is Hierarchical Linear Model? Why do nested data create analytic problems? Graphic presentation
More informationMissing Data. SPIDA 2012 Part 6 Mixed Models with R:
The best solution to the missing data problem is not to have any. Stef van Buuren, developer of mice SPIDA 2012 Part 6 Mixed Models with R: Missing Data Georges Monette 1 May 2012 Email: georges@yorku.ca
More informationHLM versus SEM Perspectives on Growth Curve Modeling. Hsueh-Sheng Wu CFDR Workshop Series August 3, 2015
HLM versus SEM Perspectives on Growth Curve Modeling Hsueh-Sheng Wu CFDR Workshop Series August 3, 2015 1 Outline What is Growth Curve Modeling (GCM) Advantages of GCM Disadvantages of GCM Graphs of trajectories
More informationCategorical Data in a Designed Experiment Part 2: Sizing with a Binary Response
Categorical Data in a Designed Experiment Part 2: Sizing with a Binary Response Authored by: Francisco Ortiz, PhD Version 2: 19 July 2018 Revised 18 October 2018 The goal of the STAT COE is to assist in
More informationGETTING STARTED WITH THE STUDENT EDITION OF LISREL 8.51 FOR WINDOWS
GETTING STARTED WITH THE STUDENT EDITION OF LISREL 8.51 FOR WINDOWS Gerhard Mels, Ph.D. mels@ssicentral.com Senior Programmer Scientific Software International, Inc. 1. Introduction The Student Edition
More informationSTATISTICS FOR PSYCHOLOGISTS
STATISTICS FOR PSYCHOLOGISTS SECTION: JAMOVI CHAPTER: USING THE SOFTWARE Section Abstract: This section provides step-by-step instructions on how to obtain basic statistical output using JAMOVI, both visually
More informationMissing Data Analysis with SPSS
Missing Data Analysis with SPSS Meng-Ting Lo (lo.194@osu.edu) Department of Educational Studies Quantitative Research, Evaluation and Measurement Program (QREM) Research Methodology Center (RMC) Outline
More informationFrequently Asked Questions Updated 2006 (TRIM version 3.51) PREPARING DATA & RUNNING TRIM
Frequently Asked Questions Updated 2006 (TRIM version 3.51) PREPARING DATA & RUNNING TRIM * Which directories are used for input files and output files? See menu-item "Options" and page 22 in the manual.
More informationlavaan: an R package for structural equation modeling
lavaan: an R package for structural equation modeling Yves Rosseel Department of Data Analysis Belgium Modern Modeling Methods Conference May 22, 2012 University of Connecticut Yves Rosseel lavaan: an
More informationBasic Medical Statistics Course
Basic Medical Statistics Course S0 SPSS Intro December 2014 Wilma Heemsbergen w.heemsbergen@nki.nl This Afternoon 13.00 ~ 15.00 SPSS lecture Short break Exercise 2 Database Example 3 Types of data Type
More information