The use of structural equation modeling to examine consumption of pornographic materials in Chinese adolescents in Hong Kong

Size: px
Start display at page:

Download "The use of structural equation modeling to examine consumption of pornographic materials in Chinese adolescents in Hong Kong"

Transcription

1 The use of structural equation modeling to examine consumption of pornographic materials in Chinese adolescents in Hong Kong Appendix 1 Creating matrices and checking for normality!prelis SYNTAX: Can be edited SY='C:\Desktop\Porn_demon\Data\PornW12.PSF' SE CO ALL OU MA=CM SM=Porn2.cm ME=Porn2.me Porn2.sd AC=Porn2.acm Command Syntax 1 SY='C:\Desktop\Porn_demon\Data\PornW 12.PSF' 2 SE Interpretation Specifies the data file (i.e., 'C:\Desktop\Porn_demon\Data\PornW12.PS F' ). Lists the observed variables. 3 CO ALL All the observed variables are continuous. 4 OU MA=CM Requests the printed output with specific results (i.e., covariance matrix, univariate statistics for all continuous variables). 5 SM=Porn2.cm Specifies the name of the covariance matrix (i.e., Porn2.cm). 6 ME=Porn2.me Specifies the name of the mean matrix (i.e., Porn2.me). 7 SD=Porn2.sd Specifies the name of the standard deviation matrix (i.e., Porn2.sd). 8 AC=Porn2.acm Specifies the name of the asymptotic covariance martix (i.e., Porn2.acm). *After typing the above commands, click the PRELIS logo

2 Appendix 2 Commands for testing a direct effect model (Model 1) To test a direct effect model, the following SIMPLIS syntax commands were used: Title: Path diagram Pornography (Model 1) Observed variables CFAIM CFAIC CFAICOM SPYDALL Covariance matrix from file Porn2.cm Asymptotic matrix from file Porn2.acm Sample size=2904 Paths > Options: MLR Lisrel output sc ef tv end of problem *After typing the above commands, click the LISREL logo Command Syntax Interpretation 1 Title: Path diagram-pornography Title of the model (i.e., Path (Model 1) diagram Pornography Model 1). 2 Observed variables CFAIM CFAIC CFAICOM SPYDALL SSEXI SSEXM Lists the observed variables (i.e., CFAIM CFAIC CFAICOM SPYDALL ). 3 Covariance matrix from file Porn1.cm 4 Asymptotic matrix from file Porn1.acm Specifies the covariance matrix (i.e., Porn1.cm). Specifies the asymptotic covariance matrix (i.e., Porn1.acm). 5 Sample size =2904 Specifies the sample size (i.e., N=2904). 6 Paths Specifies the relationship between CFAIM CFAIC CFAICOM -> dependent and independent variables. 7 Options: MLR Specifies the robust maximum likelihood estimation method. 8 Lisrel output sc ef tv Requests the LISREL printed output end of problem with the following values (i.e., sc: completely standardized solution; ef: direct and indirect effects size; tv: t values of the parameters)

3 LISREL outputs for testing a direct effect model (Model 1) Title: Path diagram-pornography (Model 1.1) Observed variables CFAIM CFAIC CFAICOM SPYDALL Covariance matrix from file Porn2.cm Asymptotic matrix from file Porn2.acm Sample size=2904 Paths -> Options: MLR Lisrel output sc ef tv end of problem Path diagram-pornography (Model 1.1) Covariance Matrix SSEXI 0.79 SSEXM Path diagram-pornography (Model 1.1) Parameter Specifications GAMMA SSEXI 1 SSEXM 2 PHI PSI 3 4 5

4 Path diagram-pornography (Model 1.1) Number of Iterations = 0 LISREL Estimates (Robust Maximum Likelihood) GAMMA SSEXI SSEXM Covariance Matrix of Y and X SSEXI 0.79 SSEXM PHI PSI Note: This matrix is diagonal Squared Multiple Correlations for Structural Equations Squared Multiple Correlations for Reduced Form

5 Goodness of Fit Statistics Degrees of Freedom = 1 Minimum Fit Function Chi-Square = (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = (P = 0.0) Satorra-Bentler Scaled Chi-Square = (P = 0.0) Chi-Square Corrected for Non-Normality = (P = 0.0) Estimated Non-centrality Parameter (NCP) = Percent Confidence Interval for NCP = ( ; ) Minimum Fit Function Value = 0.29 Population Discrepancy Function Value (F0) = Percent Confidence Interval for F0 = (0.11 ; 0.15) Root Mean Square Error of Approximation (RMSEA) = Percent Confidence Interval for RMSEA = (0.32 ; 0.39) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00 Expected Cross-Validation Index (ECVI) = Percent Confidence Interval for ECVI = (0.11 ; 0.15) ECVI for Saturated Model = ECVI for Independence Model = 1.15 Chi-Square for Independence Model with 3 Degrees of Freedom = Independence AIC = Model AIC = Saturated AIC = Independence CAIC = Model CAIC = Saturated CAIC = Normed Fit Index (NFI) = 0.89 Non-Normed Fit Index (NNFI) = 0.67 Parsimony Normed Fit Index (PNFI) = 0.30 Comparative Fit Index (CFI) = 0.89 Incremental Fit Index (IFI) = 0.89 Relative Fit Index (RFI) = 0.67 Critical N (CN) = Root Mean Square Residual (RMR) = 0.11 Standardized RMR = 0.13 Goodness of Fit Index (GFI) = 0.86 Adjusted Goodness of Fit Index (AGFI) = 0.14 Parsimony Goodness of Fit Index (PGFI) = 0.14 Path diagram-pornography (Model 1.1) Standardized Solution GAMMA SSEXI 0.65

6 SSEXM Correlation Matrix of Y and X SSEXI 1.00 SSEXM PSI Note: This matrix is diagonal Regression Matrix Y on X (Standardized) SSEXI 0.65 SSEXM Path diagram-pornography (Model 1.1) Total and Indirect Effects Total Effects of X on Y SSEXI SSEXM BETA*BETA' is not Pos. Def., Stability Index cannot be Computed Path diagram-pornography (Model 1.1) Standardized Total and Indirect Effects Standardized Total Effects of X on Y SSEXI 0.65 SSEXM Time used: Seconds

7 Appendix 3 Commands for testing a direct and indirect effects model (Model 2) To test a direct and indirect effects model, the following SIMPLIS syntax commands were used: Title: Path diagram Pornography (Model 2) Observed variables CFAIM CFAIC CFAICOM SPYDALL Covariance matrix from file Porn2.cm Asymptotic matrix from file Porn2.acm Sample size=2904 Paths CFAIMALL > SPYDALL SPYDALL > Options: MLR Lisrel output sc ef tv end of problem *After typing the above commands, click the LISREL logo Command Syntax Interpretation 1 Title: Path diagram- Title of the model (i.e., Path diagram Pornography (Model 2) Pornography Model 2). 2 Observed variables CFAIM CFAIC CFAICOM SSEXI SSEXM SPYDALL 3 Covariance matrix from file Porn2.cm 4 Asymptotic matrix from file Porn2.acm Lists the observed variables (i.e., CFAIM CFAIC CFAICOM SPYDALL). Specifies the covariance matrix (i.e., Porn2.cm). Specifies the asymptotic covariance matrix (i.e., Porn2.acm). 5 Sample size =2904 Specifies the sample size (i.e., N=2904). 6 Paths Specifies the relationships between CFAIMALL ->SPYDALL SSEXI dependent and independent variables. SSEXM SPYDALL -> 7 Options: MLR Specifies the robust maximum likelihood 8 Lisrel output sc ef tv end of problem estimation method. Requests the LISREL printed output with the following values (i.e., sc: completely standardized solution; ef: direct and indirect effects size; tv: t values of the parameters)

8 LISREL outputs for testing a direct and indirect effect model (Model 2) Title: Path diagram-pornography (Model 2.1) Observed variables CFAIM CFAIC CFAICOM SPYDALL Covariance matrix from file Porn2.cm Asymptotic matrix from file Porn2.acm Sample size=2904 Paths -> SPYDALL SPYDALL -> Options: MLR Lisrel output sc ef tv end of problem Path diagram-pornography (Model 2.1) Covariance Matrix SPYDALL SPYDALL 0.45 SSEXI SSEXM Path diagram-pornography (Model 2.1) Parameter Specifications BETA SPYDALL SPYDALL SSEXI SSEXM GAMMA SPYDALL 3 SSEXI 4

9 SSEXM 5 PHI PSI 6 SPYDALL Path diagram-pornography (Model 2.1) Number of Iterations = 0 LISREL Estimates (Robust Maximum Likelihood) BETA SPYDALL SPYDALL SSEXI SSEXM (0.03) GAMMA SPYDALL SSEXI SSEXM Covariance Matrix of Y and X SPYDALL SPYDALL 0.45 SSEXI SSEXM

10 PHI PSI Note: This matrix is diagonal. SPYDALL (0.01) Squared Multiple Correlations for Structural Equations SPYDALL Squared Multiple Correlations for Reduced Form SPYDALL Reduced Form SPYDALL SSEXI SSEXM Goodness of Fit Statistics Degrees of Freedom = 1 Minimum Fit Function Chi-Square = (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = (P = 0.0) Satorra-Bentler Scaled Chi-Square = (P = 0.0) Chi-Square Corrected for Non-Normality = (P = 0.0) Estimated Non-centrality Parameter (NCP) = Percent Confidence Interval for NCP = ( ; ) Minimum Fit Function Value = 0.29 Population Discrepancy Function Value (F0) = Percent Confidence Interval for F0 = (0.11 ; 0.15)

11 Root Mean Square Error of Approximation (RMSEA) = Percent Confidence Interval for RMSEA = (0.33 ; 0.39) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00 Expected Cross-Validation Index (ECVI) = Percent Confidence Interval for ECVI = (0.11 ; 0.16) ECVI for Saturated Model = ECVI for Independence Model = 1.68 Chi-Square for Independence Model with 6 Degrees of Freedom = Independence AIC = Model AIC = Saturated AIC = Independence CAIC = Model CAIC = Saturated CAIC = Normed Fit Index (NFI) = 0.92 Non-Normed Fit Index (NNFI) = 0.55 Parsimony Normed Fit Index (PNFI) = 0.15 Comparative Fit Index (CFI) = 0.92 Incremental Fit Index (IFI) = 0.92 Relative Fit Index (RFI) = 0.55 Critical N (CN) = Root Mean Square Residual (RMR) = Standardized RMR = 0.10 Goodness of Fit Index (GFI) = 0.89 Adjusted Goodness of Fit Index (AGFI) = Parsimony Goodness of Fit Index (PGFI) = Path diagram-pornography (Model 2.1) Standardized Solution BETA SPYDALL SPYDALL SSEXI SSEXM GAMMA SPYDALL 0.54 SSEXI 0.61 SSEXM Correlation Matrix of Y and X SPYDALL

12 SPYDALL 1.00 SSEXI SSEXM PSI Note: This matrix is diagonal. SPYDALL Regression Matrix Y on X (Standardized) SPYDALL 0.54 SSEXI 0.65 SSEXM Path diagram-pornography (Model 2.1) Total and Indirect Effects Total Effects of X on Y SPYDALL SSEXI SSEXM Indirect Effects of X on Y SPYDALL - - SSEXI 0.04 (0.01) 4.12 SSEXM (0.01) Total Effects of Y on Y SPYDALL SPYDALL SSEXI

13 4.15 SSEXM (0.03) Largest Eigenvalue of B*B' (Stability Index) is Path diagram-pornography (Model 2.1) Standardized Total and Indirect Effects Standardized Total Effects of X on Y SPYDALL 0.54 SSEXI 0.65 SSEXM Standardized Indirect Effects of X on Y SPYDALL - - SSEXI 0.04 SSEXM Standardized Total Effects of Y on Y SPYDALL SPYDALL SSEXI SSEXM Time used: Seconds

14 Appendix 4 Commands for testing an additional path (Model 1a) To test the additional path in Model 1, the following SIMPLIS syntax commands were used: Title: Path diagram Pornography (Model 1a) Observed variables CFAIM CFAIC CFAICOM SPYDALL Covariance matrix from file Porn2.cm Asymptotic matrix from file Porn2.acm Sample size=2904 Paths > SSEXM >SSEXI Options: MLR Lisrel output sc ef tv end of problem *After typing the above commands, click the LISREL logo Command Syntax Interpretation 1 Title: Path diagram-pornography Title of the model (i.e., Path (Model 1) diagram Pornography Model 1). 2 Observed variables CFAIM CFAIC CFAICOM SPYDALL SSEXI SSEXM Lists the observed variables (i.e., CFAIM CFAIC CFAICOM SPYDALL ). 3 Covariance matrix from file Porn2.cm Specifies the covariance matrix (i.e., Porn2.cm). 4 Asymptotic matrix from file Porn2.acm Specifies the asymptotic covariance matrix (i.e., Porn2.acm). 5 Sample size =2904 Specifies the sample size (i.e., N=2904). 6 Paths CFAIM CFAIC CFAICOM -> Specifies the relationship between dependent and independent variables. 7 SSEXM -> SSEXI Specifies the relationship between dependent variables. 8 Options: MLR Specifies the robust maximum likelihood estimation method. 9 Lisrel output sc ef tv end of problem Requests the LISREL printed output with the following values (i.e., sc: completely standardized solution; ef: direct and indirect effects size; tv: t values of the parameters)

15 LISREL outputs for testing an additional path (Model 1a) Title: Path diagram-pornography (Model 1a) Observed variables CFAIM CFAIC CFAICOM SPYDALL Covariance matrix from file Porn2.cm Asymptotic matrix from file Porn2.acm Sample size=2904 Paths -> SSEXM -> SSEXI Options: MLR Lisrel output sc ef tv end of problem Path diagram-pornography (Model 1a) Covariance Matrix SSEXI 0.79 SSEXM Path diagram-pornography (Model 1a) Parameter Specifications BETA SSEXI 0 1 SSEXM 0 0 GAMMA SSEXI 2 SSEXM 3 PHI 4

16 PSI 5 6 Path diagram-pornography (Model 1a) Number of Iterations = 0 LISREL Estimates (Robust Maximum Likelihood) BETA SSEXI SSEXM GAMMA SSEXI SSEXM Covariance Matrix of Y and X SSEXI 0.79 SSEXM PHI PSI Note: This matrix is diagonal (0.01)

17 Squared Multiple Correlations for Structural Equations Squared Multiple Correlations for Reduced Form Reduced Form SSEXI SSEXM Goodness of Fit Statistics Degrees of Freedom = 0 Minimum Fit Function Chi-Square = 0.00 (P = 1.00) Normal Theory Weighted Least Squares Chi-Square = 0.00 (P = 1.00) Satorra-Bentler Scaled Chi-Square = 0.0 (P = 1.00) The Model is Saturated, the Fit is Perfect! Path diagram-pornography (Model 1a) Standardized Solution BETA SSEXI SSEXM GAMMA SSEXI 0.41 SSEXM Correlation Matrix of Y and X

18 SSEXI 1.00 SSEXM PSI Note: This matrix is diagonal Regression Matrix Y on X (Standardized) SSEXI 0.65 SSEXM Path diagram-pornography (Model 1a) Total and Indirect Effects Total Effects of X on Y SSEXI SSEXM Indirect Effects of X on Y SSEXI SSEXM - - Total Effects of Y on Y SSEXI SSEXM Largest Eigenvalue of B*B' (Stability Index) is Path diagram-pornography (Model 1a) Standardized Total and Indirect Effects

19 Standardized Total Effects of X on Y SSEXI 0.65 SSEXM Standardized Indirect Effects of X on Y SSEXI 0.24 SSEXM - - Standardized Total Effects of Y on Y SSEXI SSEXM Time used: Seconds

The Role of Leader Motivating Language in Employee Absenteeism (Mayfield: 2009)

The Role of Leader Motivating Language in Employee Absenteeism (Mayfield: 2009) DATE: 12/15/2009 TIME: 5:50 Page 1 LISREL 8.80 (STUDENT EDITION) BY Karl G. J reskog & Dag S rbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue,

More information

DATE: 9/ L I S R E L 8.80

DATE: 9/ L I S R E L 8.80 98 LAMPIRAN 3 STRUCTURAL EQUATION MODEL ONE CONGINERIC Use of this program is subject to the terms specified in the Convention. Universal Copyright 9/2017 DATE: 9/ TIME: 20:22 Website: www.ssicentral.com

More information

C:\Users\Rex Kline\AppData\Local\Temp\AmosTemp\AmosScratch.amw. Date: Friday, December 5, 2014 Time: 11:20:30 AM

C:\Users\Rex Kline\AppData\Local\Temp\AmosTemp\AmosScratch.amw. Date: Friday, December 5, 2014 Time: 11:20:30 AM Page 1 of 7 C:\Users\Rex Kline\AppData\Local\Temp\AmosTemp\AmosScratch.amw Analysis Summary Date and Time Date: Friday, December 5, 2014 Time: 11:20:30 AM Title Groups Group number 1 (Group number 1) Notes

More information

SC705: Advanced Statistics Instructor: Natasha Sarkisian Class notes: Model Building Strategies

SC705: Advanced Statistics Instructor: Natasha Sarkisian Class notes: Model Building Strategies SC705: Advanced Statistics Instructor: Natasha Sarkisian Class notes: Model Building Strategies Model Diagnostics The model diagnostics and improvement strategies discussed here apply to both measurement

More information

CONFIRMATORY FACTOR ANALYSIS

CONFIRMATORY FACTOR ANALYSIS 1 CONFIRMATORY FACTOR ANALYSIS The purpose of confirmatory factor analysis (CFA) is to explain the pattern of associations among a set of observed variables in terms of a smaller number of underlying latent

More information

THE GENERAL STRUCTURAL EQUATION MODEL WITH LATENT VARIATES

THE GENERAL STRUCTURAL EQUATION MODEL WITH LATENT VARIATES THE GENERAL STRUCTURAL EQUATION MODEL WITH LATENT VARIATES I. Specification: A full structural equation model with latent variables consists of two parts: a latent variable model (which specifies the relations

More information

EVALUATION OF STRUCTURAL EQUATION MODELS

EVALUATION OF STRUCTURAL EQUATION MODELS 1 EVALUATION OF STRUCTURAL EQUATION MODELS I. Issues related to the initial specification of theoretical models of interest 1. Model specification: a. Measurement model: (i) EFA vs. CFA (ii) reflective

More information

Instrumental variables regression on the Poverty data

Instrumental variables regression on the Poverty data Instrumental variables regression on the Poverty data /********************** poverty2.sas **************************/ options linesize=79 noovp formdlim='-' nodate; title 'UN Poverty Data: Instrumental

More information

Evaluation of structural equation models. Hans Baumgartner Penn State University

Evaluation of structural equation models. Hans Baumgartner Penn State University Evaluation of structural equation models Hans Baumgartner Penn State University Issues related to the initial specification of theoretical models of interest Model specification: Measurement model: EFA

More information

Regression without measurement error using proc calis

Regression without measurement error using proc calis Regression without measurement error using proc calis /* calculus2.sas */ options linesize=79 pagesize=500 noovp formdlim='_'; title 'Calculus 2: Regression with no measurement error'; title2 ''; data

More information

SAS Example 3: Deliberately create numerical problems

SAS Example 3: Deliberately create numerical problems SAS Example 3: Deliberately create numerical problems Four experiments 1. Try to fit this model, failing the parameter count rule. 2. Set φ 12 =0 to pass the parameter count rule, but still not identifiable.

More information

THE GENERAL STRUCTURAL EQUATION MODEL WITH LATENT VARIATES

THE GENERAL STRUCTURAL EQUATION MODEL WITH LATENT VARIATES THE GENERAL STRUCTURAL EQUATION MODEL WITH LATENT VARIATES I. Specification: A full structural equation model with latent variables consists of two parts: a latent variable model (which specifies the relations

More information

Psychology 454: Latent Variable Modeling How do you know if a model works?

Psychology 454: Latent Variable Modeling How do you know if a model works? Psychology 454: Latent Variable Modeling How do you know if a model works? William Revelle Department of Psychology Northwestern University Evanston, Illinois USA November, 2012 1 / 18 Outline 1 Goodness

More information

UNIVERSITY OF TORONTO MISSISSAUGA April 2009 Examinations STA431H5S Professor Jerry Brunner Duration: 3 hours

UNIVERSITY OF TORONTO MISSISSAUGA April 2009 Examinations STA431H5S Professor Jerry Brunner Duration: 3 hours Name (Print): Student Number: Signature: Last/Surname First /Given Name UNIVERSITY OF TORONTO MISSISSAUGA April 2009 Examinations STA431H5S Professor Jerry Brunner Duration: 3 hours Aids allowed: Calculator

More information

Introduction to Confirmatory Factor Analysis

Introduction to Confirmatory Factor Analysis Introduction to Confirmatory Factor Analysis In Exploratory FA, the analyst has no strong prior notion of the structure of the factor solution the goal is to infer factor structure from the patterns of

More information

Confirmatory Factor Analysis. Psych 818 DeShon

Confirmatory Factor Analysis. Psych 818 DeShon Confirmatory Factor Analysis Psych 818 DeShon Purpose Takes factor analysis a few steps further. Impose theoretically interesting constraints on the model and examine the resulting fit of the model with

More information

* IVEware Analysis Examples Replication for ASDA 2nd Edition * Berglund April 2017 * Chapter 13 ;

* IVEware Analysis Examples Replication for ASDA 2nd Edition * Berglund April 2017 * Chapter 13 ; IVEware Analysis Example Replication C13 * IVEware Analysis Examples Replication for ASDA 2nd Edition * Berglund April 2017 * Chapter 13 ; * 13.3.3 Alternative Approaches to Fitting GLMMs to Survey Data:

More information

Preface. List of examples

Preface. List of examples Contents Preface List of examples i xix 1 LISREL models and methods 1 1.1 The general LISREL model 1 Assumptions 2 The covariance matrix of the observations as implied by the LISREL model 3 Fixed, free,

More information

Structural Equation Modeling Lab 5 In Class Modification Indices Example

Structural Equation Modeling Lab 5 In Class Modification Indices Example Structural Equation Modeling Lab 5 In Class Modification Indices Example. Model specifications sntax TI Modification Indices DA NI=0 NO=0 MA=CM RA FI='E:\Teaching\SEM S09\Lab 5\jsp6.psf' SE 7 6 5 / MO

More information

This course. Tutors. Jon Heron, PhD (Bristol) Anna Brown, PhD (Cambridge)

This course. Tutors. Jon Heron, PhD (Bristol) Anna Brown, PhD (Cambridge) This course The course is funded by the ESRC RDI and hosted by The Psychometrics Centre Tutors Jon Heron, PhD (Bristol) jon.heron@bristol.ac.uk Anna Brown, PhD (Cambridge) ab936@medschl.cam.ac.uk Tim Croudace,

More information

Multi-group analyses for measurement invariance parameter estimates and model fit (ML)

Multi-group analyses for measurement invariance parameter estimates and model fit (ML) LBP-TBQ: Supplementary digital content 8 Multi-group analyses for measurement invariance parameter estimates and model fit (ML) Medication data Multi-group CFA analyses were performed with the 16-item

More information

Hypothesis Testing for Var-Cov Components

Hypothesis Testing for Var-Cov Components Hypothesis Testing for Var-Cov Components When the specification of coefficients as fixed, random or non-randomly varying is considered, a null hypothesis of the form is considered, where Additional output

More information

SEM Day 1 Lab Exercises SPIDA 2007 Dave Flora

SEM Day 1 Lab Exercises SPIDA 2007 Dave Flora SEM Day 1 Lab Exercises SPIDA 2007 Dave Flora 1 Today we will see how to estimate CFA models and interpret output using both SAS and LISREL. In SAS, commands for specifying SEMs are given using linear

More information

Evaluating the Sensitivity of Goodness-of-Fit Indices to Data Perturbation: An Integrated MC-SGR Approach

Evaluating the Sensitivity of Goodness-of-Fit Indices to Data Perturbation: An Integrated MC-SGR Approach Evaluating the Sensitivity of Goodness-of-Fit Indices to Data Perturbation: An Integrated MC-SGR Approach Massimiliano Pastore 1 and Luigi Lombardi 2 1 Department of Psychology University of Cagliari Via

More information

sempower Manual Morten Moshagen

sempower Manual Morten Moshagen sempower Manual Morten Moshagen 2018-03-22 Power Analysis for Structural Equation Models Contact: morten.moshagen@uni-ulm.de Introduction sempower provides a collection of functions to perform power analyses

More information

Multi-sample structural equation models with mean structures, with special emphasis on assessing measurement invariance in cross-national research

Multi-sample structural equation models with mean structures, with special emphasis on assessing measurement invariance in cross-national research 1 Multi-sample structural equation models with mean structures, with special emphasis on assessin measurement invariance in cross-national research Measurement invariance measurement invariance: whether

More information

Introduction to Structural Equation Modeling with Latent Variables

Introduction to Structural Equation Modeling with Latent Variables SAS/STAT 9.2 User s Guide Introduction to Structural Equation Modeling with Latent Variables (Book Excerpt) SAS Documentation This document is an individual chapter from SAS/STAT 9.2 User s Guide. The

More information

Multiple Group CFA Invariance Example (data from Brown Chapter 7) using MLR Mplus 7.4: Major Depression Criteria across Men and Women (n = 345 each)

Multiple Group CFA Invariance Example (data from Brown Chapter 7) using MLR Mplus 7.4: Major Depression Criteria across Men and Women (n = 345 each) Multiple Group CFA Invariance Example (data from Brown Chapter 7) using MLR Mplus 7.4: Major Depression Criteria across Men and Women (n = 345 each) 9 items rated by clinicians on a scale of 0 to 8 (0

More information

Multiple group models for ordinal variables

Multiple group models for ordinal variables Multiple group models for ordinal variables 1. Introduction In practice, many multivariate data sets consist of observations of ordinal variables rather than continuous variables. Most statistical methods

More information

Pelatihan Statistika. Jonathan Sarwono

Pelatihan Statistika. Jonathan Sarwono Pelatihan Statistika Jonathan Sarwono Model - Model Penelitian dalam Riset Lanjutan 1. Model Dasar Hubungan Antar Variabel 2. Model Dasa dalam Analisis Jalur 3. Model dalam Structural Equiation Modeling

More information

RESMA course Introduction to LISREL. Harry Ganzeboom RESMA Data Analysis & Report #4 February

RESMA course Introduction to LISREL. Harry Ganzeboom RESMA Data Analysis & Report #4 February RESMA course Introduction to LISREL Harry Ganzeboom RESMA Data Analysis & Report #4 February 17 2009 LISREL SEM: Simultaneous [Structural] Equations Model: A system of linear equations ( causal model )

More information

Structural Equation Modeling and Confirmatory Factor Analysis. Types of Variables

Structural Equation Modeling and Confirmatory Factor Analysis. Types of Variables /4/04 Structural Equation Modeling and Confirmatory Factor Analysis Advanced Statistics for Researchers Session 3 Dr. Chris Rakes Website: http://csrakes.yolasite.com Email: Rakes@umbc.edu Twitter: @RakesChris

More information

Psychology 454: Latent Variable Modeling How do you know if a model works?

Psychology 454: Latent Variable Modeling How do you know if a model works? Psychology 454: Latent Variable Modeling How do you know if a model works? William Revelle Department of Psychology Northwestern University Evanston, Illinois USA October, 2017 1 / 33 Outline Goodness

More information

Structural Equation Modeling. Chapter 11. Comparing Means. Merle Canfield

Structural Equation Modeling. Chapter 11. Comparing Means. Merle Canfield Structural Equation Modeling Chapter 11 Comparing Means Merle Canfield study. This chapter demonstrates a method for comparing the means of two factors in a pre/post The V999 measured variable of EQS has

More information

Factor analysis. George Balabanis

Factor analysis. George Balabanis Factor analysis George Balabanis Key Concepts and Terms Deviation. A deviation is a value minus its mean: x - mean x Variance is a measure of how spread out a distribution is. It is computed as the average

More information

Exploring Cultural Differences with Structural Equation Modelling

Exploring Cultural Differences with Structural Equation Modelling Exploring Cultural Differences with Structural Equation Modelling Wynne W. Chin University of Calgary and City University of Hong Kong 1996 IS Cross Cultural Workshop slide 1 The objectives for this presentation

More information

Structural Equation Modeling

Structural Equation Modeling CHAPTER 23 Structural Equation Modeling JODIE B. ULLMAN AND PETER M. BENTLER A FOUR-STAGE GENERAL PROCESS OF MODELING 663 MODEL ESTIMATION TECHNIQUES AND TEST STATISTICS 667 MODEL EVALUATION 671 MODEL

More information

The comparison of estimation methods on the parameter estimates and fit indices in SEM model under 7-point Likert scale

The comparison of estimation methods on the parameter estimates and fit indices in SEM model under 7-point Likert scale The comparison of estimation methods on the parameter estimates and fit indices in SEM model under 7-point Likert scale Piotr Tarka Abstract In this article, the author discusses the issues and problems

More information

SEM Analysis of Epigenetic Data

SEM Analysis of Epigenetic Data SEM Analysis of Epigenetic Data By Azadeh Chizarifard Department of Statistics Uppsala University Supervisors: Åsa Johansson, Rolf Larsson 2014 Abstract DNA methylation as well as glucosylceramide has

More information

Chapter 13 Introduction to Structural Equation Modeling

Chapter 13 Introduction to Structural Equation Modeling Chapter 13 Introduction to Structural Equation Modeling Chapter Contents OVERVIEW................................... 203 COMPARISON OF THE CALIS AND SYSLIN PROCEDURES...... 203 MODEL SPECIFICATION...........................

More information

Structural Equation Modelling

Structural Equation Modelling Slide Email: jkanglim@unimelb.edu.au Office: Room 0 Redmond Barry Building Website: http://jeromyanglim.googlepages.com/ Appointments: For appointments regarding course or with the application of statistics

More information

Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis

Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis Advanced Statistics for Researchers Session 3 Dr. Chris Rakes Website: http://csrakes.yolasite.com Email: Rakes@umbc.edu Twitter:

More information

Compiled by: Assoc. Prof. Dr Bahaman Abu Samah Department of Professional Developmentand Continuing Education Faculty of Educational Studies

Compiled by: Assoc. Prof. Dr Bahaman Abu Samah Department of Professional Developmentand Continuing Education Faculty of Educational Studies Compiled by: Assoc. Prof. Dr Bahaman Abu Samah Department of Professional Developmentand Continuing Education Faculty of Educational Studies Universiti Putra Malaysia Serdang Structural Equation Modeling

More information

PLEASE SCROLL DOWN FOR ARTICLE. Full terms and conditions of use:

PLEASE SCROLL DOWN FOR ARTICLE. Full terms and conditions of use: This article was downloaded by: [Howell, Roy][Texas Tech University] On: 15 December 2009 Access details: Access Details: [subscription number 907003254] Publisher Psychology Press Informa Ltd Registered

More information

Lecture notes I: Measurement invariance 1

Lecture notes I: Measurement invariance 1 Lecture notes I: Measurement Invariance (RM20; Jelte Wicherts). 1 Lecture notes I: Measurement invariance 1 Literature. Mellenbergh, G. J. (1989). Item bias and item response theory. International Journal

More information

2/26/2017. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2

2/26/2017. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 What is SEM? When should we use SEM? What can SEM tell us? SEM Terminology and Jargon Technical Issues Types of SEM Models Limitations

More information

Systematic error, of course, can produce either an upward or downward bias.

Systematic error, of course, can produce either an upward or downward bias. Brief Overview of LISREL & Related Programs & Techniques (Optional) Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised April 6, 2015 STRUCTURAL AND MEASUREMENT MODELS:

More information

Title. Description. Remarks and examples. stata.com. stata.com. Variable notation. methods and formulas for sem Methods and formulas for sem

Title. Description. Remarks and examples. stata.com. stata.com. Variable notation. methods and formulas for sem Methods and formulas for sem Title stata.com methods and formulas for sem Methods and formulas for sem Description Remarks and examples References Also see Description The methods and formulas for the sem commands are presented below.

More information

Introduction to Confirmatory Factor Analysis

Introduction to Confirmatory Factor Analysis Introduction to Confirmatory Factor Analysis Multivariate Methods in Education ERSH 8350 Lecture #12 November 16, 2011 ERSH 8350: Lecture 12 Today s Class An Introduction to: Confirmatory Factor Analysis

More information

Latent Variable Analysis

Latent Variable Analysis Latent Variable Analysis Path Analysis Recap I. Path Diagram a. Exogeneous vs. Endogeneous Variables b. Dependent vs, Independent Variables c. Recursive vs. on-recursive Models II. Structural (Regression)

More information

CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA

CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA Examples: Multilevel Modeling With Complex Survey Data CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA Complex survey data refers to data obtained by stratification, cluster sampling and/or

More information

SEM Day 3 Lab Exercises SPIDA 2007 Dave Flora

SEM Day 3 Lab Exercises SPIDA 2007 Dave Flora SEM Day 3 Lab Exercises SPIDA 2007 Dave Flora 1 Today we will see how to estimate SEM conditional latent trajectory models and interpret output using both SAS and LISREL. Exercise 1 Using SAS PROC CALIS,

More information

Inference using structural equations with latent variables

Inference using structural equations with latent variables This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this

More information

MLMED. User Guide. Nicholas J. Rockwood The Ohio State University Beta Version May, 2017

MLMED. User Guide. Nicholas J. Rockwood The Ohio State University Beta Version May, 2017 MLMED User Guide Nicholas J. Rockwood The Ohio State University rockwood.19@osu.edu Beta Version May, 2017 MLmed is a computational macro for SPSS that simplifies the fitting of multilevel mediation and

More information

Step 2: Select Analyze, Mixed Models, and Linear.

Step 2: Select Analyze, Mixed Models, and Linear. Example 1a. 20 employees were given a mood questionnaire on Monday, Wednesday and again on Friday. The data will be first be analyzed using a Covariance Pattern model. Step 1: Copy Example1.sav data file

More information

Preface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of

Preface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of Preface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of Probability Sampling Procedures Collection of Data Measures

More information

Introduction to Structural Equation Modelling Answers to Exercises

Introduction to Structural Equation Modelling Answers to Exercises Introduction to Structural Equation Modelling Answers to Exercises John Fox Applied Statistics With R WU-Wien, May/June 2006 1. 4.1 (a) Recursive y 3 = γ 31 x 1 + γ 32 x 2 + ε 6 y 4 = γ 41 x 1 + γ 42 x

More information

Package semgof. February 20, 2015

Package semgof. February 20, 2015 Package semgof February 20, 2015 Version 0.2-0 Date 2012-08-06 Title Goodness-of-fit indexes for structural equation models Author Elena Bertossi Maintainer Elena Bertossi

More information

Stat 5101 Lecture Notes

Stat 5101 Lecture Notes Stat 5101 Lecture Notes Charles J. Geyer Copyright 1998, 1999, 2000, 2001 by Charles J. Geyer May 7, 2001 ii Stat 5101 (Geyer) Course Notes Contents 1 Random Variables and Change of Variables 1 1.1 Random

More information

Related Concepts: Lecture 9 SEM, Statistical Modeling, AI, and Data Mining. I. Terminology of SEM

Related Concepts: Lecture 9 SEM, Statistical Modeling, AI, and Data Mining. I. Terminology of SEM Lecture 9 SEM, Statistical Modeling, AI, and Data Mining I. Terminology of SEM Related Concepts: Causal Modeling Path Analysis Structural Equation Modeling Latent variables (Factors measurable, but thru

More information

Manual Of The Program FACTOR. v Windows XP/Vista/W7/W8. Dr. Urbano Lorezo-Seva & Dr. Pere Joan Ferrando

Manual Of The Program FACTOR. v Windows XP/Vista/W7/W8. Dr. Urbano Lorezo-Seva & Dr. Pere Joan Ferrando Manual Of The Program FACTOR v.9.20 Windows XP/Vista/W7/W8 Dr. Urbano Lorezo-Seva & Dr. Pere Joan Ferrando urbano.lorenzo@urv.cat perejoan.ferrando@urv.cat Departament de Psicologia Universitat Rovira

More information

Introduction to Structural Equation Models with Latent Variables. Scope. Statistics in Social Sciences. Measuring errors in Social Sciences

Introduction to Structural Equation Models with Latent Variables. Scope. Statistics in Social Sciences. Measuring errors in Social Sciences 2 Introduction to Structural Equation Models with Latent Variables Josep Allepús Benevento, May 5 th 24 Scope Statistics in Social Sciences Introduction to Structural Equation Models with Latent Variables

More information

Longitudinal Invariance CFA (using MLR) Example in Mplus v. 7.4 (N = 151; 6 items over 3 occasions)

Longitudinal Invariance CFA (using MLR) Example in Mplus v. 7.4 (N = 151; 6 items over 3 occasions) Longitudinal Invariance CFA (using MLR) Example in Mplus v. 7.4 (N = 151; 6 items over 3 occasions) CLP 948 Example 7b page 1 These data measuring a latent trait of social functioning were collected at

More information

Case of single exogenous (iv) variable (with single or multiple mediators) iv à med à dv. = β 0. iv i. med i + α 1

Case of single exogenous (iv) variable (with single or multiple mediators) iv à med à dv. = β 0. iv i. med i + α 1 Mediation Analysis: OLS vs. SUR vs. ISUR vs. 3SLS vs. SEM Note by Hubert Gatignon July 7, 2013, updated November 15, 2013, April 11, 2014, May 21, 2016 and August 10, 2016 In Chap. 11 of Statistical Analysis

More information

Confirmatory Factor Models (CFA: Confirmatory Factor Analysis)

Confirmatory Factor Models (CFA: Confirmatory Factor Analysis) Confirmatory Factor Models (CFA: Confirmatory Factor Analysis) Today s topics: Comparison of EFA and CFA CFA model parameters and identification CFA model estimation CFA model fit evaluation CLP 948: Lecture

More information

Introduction to Structural Equation Modeling Dominique Zephyr Applied Statistics Lab

Introduction to Structural Equation Modeling Dominique Zephyr Applied Statistics Lab Applied Statistics Lab Introduction to Structural Equation Modeling Dominique Zephyr Applied Statistics Lab SEM Model 3.64 7.32 Education 2.6 Income 2.1.6.83 Charac. of Individuals 1 5.2e-06 -.62 2.62

More information

Maximum Likelihood Estimation; Robust Maximum Likelihood; Missing Data with Maximum Likelihood

Maximum Likelihood Estimation; Robust Maximum Likelihood; Missing Data with Maximum Likelihood Maximum Likelihood Estimation; Robust Maximum Likelihood; Missing Data with Maximum Likelihood PRE 906: Structural Equation Modeling Lecture #3 February 4, 2015 PRE 906, SEM: Estimation Today s Class An

More information

Essentials of Structural Equation Modeling

Essentials of Structural Equation Modeling University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Zea E-Books Zea E-Books 3-12-2018 Essentials of Structural Equation Modeling Mustafa Emre Civelek Istanbul Commerce University,

More information

An Introduction to Path Analysis

An Introduction to Path Analysis An Introduction to Path Analysis PRE 905: Multivariate Analysis Lecture 10: April 15, 2014 PRE 905: Lecture 10 Path Analysis Today s Lecture Path analysis starting with multivariate regression then arriving

More information

Improper Solutions in Exploratory Factor Analysis: Causes and Treatments

Improper Solutions in Exploratory Factor Analysis: Causes and Treatments Improper Solutions in Exploratory Factor Analysis: Causes and Treatments Yutaka Kano Faculty of Human Sciences, Osaka University Suita, Osaka 565, Japan. email: kano@hus.osaka-u.ac.jp Abstract: There are

More information

Chapter 3: Testing alternative models of data

Chapter 3: Testing alternative models of data Chapter 3: Testing alternative models of data William Revelle Northwestern University Prepared as part of course on latent variable analysis (Psychology 454) and as a supplement to the Short Guide to R

More information

CONFIRMATORY FACTOR ANALYSIS

CONFIRMATORY FACTOR ANALYSIS 1 CONFIRMATORY FACTOR ANALYSIS The purpose of confirmatory factor analysis (CFA) is to explain the pattern of associations among a set of observe variables in terms of a smaller number of unerlying latent

More information

USING AN EIGENVALUE DISTRIBUTION TO COMPARE COVARIANCE STRUCTURE MATRICES DISSERTATION. Presented to the Graduate Council of the

USING AN EIGENVALUE DISTRIBUTION TO COMPARE COVARIANCE STRUCTURE MATRICES DISSERTATION. Presented to the Graduate Council of the 379 /42/J USING AN EIGENVALUE DISTRIBUTION TO COMPARE COVARIANCE STRUCTURE MATRICES DISSERTATION Presented to the Graduate Council of the University of North Texas in Partial Fulfillment of the Requirements

More information

ADVANCED C. MEASUREMENT INVARIANCE SEM REX B KLINE CONCORDIA

ADVANCED C. MEASUREMENT INVARIANCE SEM REX B KLINE CONCORDIA ADVANCED SEM C. MEASUREMENT INVARIANCE REX B KLINE CONCORDIA C C2 multiple model 2 data sets simultaneous C3 multiple 2 populations 2 occasions 2 methods C4 multiple unstandardized constrain to equal fit

More information

STRUCTURAL EQUATION MODELING

STRUCTURAL EQUATION MODELING NIELS J. BLUNCH Introduction to STRUCTURAL EQUATION MODELING USING IBM SPSS STATISTICS AND EQS 00_Blunch_Prelims.indd 3 9/22/2015 2:43:42 PM SAGE Publications Ltd 1 Oliver s Yard 55 City Road London EC1Y

More information

A Cautionary Note on the Use of LISREL s Automatic Start Values in Confirmatory Factor Analysis Studies R. L. Brown University of Wisconsin

A Cautionary Note on the Use of LISREL s Automatic Start Values in Confirmatory Factor Analysis Studies R. L. Brown University of Wisconsin A Cautionary Note on the Use of LISREL s Automatic Start Values in Confirmatory Factor Analysis Studies R. L. Brown University of Wisconsin The accuracy of parameter estimates provided by the major computer

More information

Introduction to Structural Equation Modeling

Introduction to Structural Equation Modeling Introduction to Structural Equation Modeling Notes Prepared by: Lisa Lix, PhD Manitoba Centre for Health Policy Topics Section I: Introduction Section II: Review of Statistical Concepts and Regression

More information

DIAGNOSTICS OF THE ERROR FACTOR COVARIANCES OTTÓ HAJDU 1

DIAGNOSTICS OF THE ERROR FACTOR COVARIANCES OTTÓ HAJDU 1 DIAGNOSTICS OF THE ERROR FACTOR COVARIANCES OTTÓ HAJDU In this paper we explore initial simple factor structure by the means of the so-called EPIC factor extraction method and the orthosim orthogonal rotational

More information

STRUCTURAL EQUATION MODEL (SEM)

STRUCTURAL EQUATION MODEL (SEM) STRUCTURAL EQUATION MODEL (SEM) V. Čekanavičius, G. Murauskas 1 PURPOSE OF SEM To check if the model of possible variable dependencies matches data. SEM can contain latent (directly unobservable) variables.

More information

Fit Indices Versus Test Statistics

Fit Indices Versus Test Statistics MULTIVARIATE BEHAVIORAL RESEARCH, 40(1), 115 148 Copyright 2005, Lawrence Erlbaum Associates, Inc. Fit Indices Versus Test Statistics Ke-Hai Yuan University of Notre Dame Model evaluation is one of the

More information

An Introduction to Mplus and Path Analysis

An Introduction to Mplus and Path Analysis An Introduction to Mplus and Path Analysis PSYC 943: Fundamentals of Multivariate Modeling Lecture 10: October 30, 2013 PSYC 943: Lecture 10 Today s Lecture Path analysis starting with multivariate regression

More information

Arturo, GARCÍA-SANTILLÁN. 1. Introduction

Arturo, GARCÍA-SANTILLÁN. 1. Introduction EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 10, No. 2, 2017, 167-198 ISSN 1307-5543 www.ejpam.com Published by New York Business Global Measuring set latent variables that explain attitude toward

More information

Online appendix to accompany:

Online appendix to accompany: Online appendix to accompany: Preacher, K. J., & Hancock, G. R. (submitted). Meaningful aspects of change as novel random coefficients: A general method for reparameterizing longitudinal models. Contents

More information

over Time line for the means). Specifically, & covariances) just a fixed variance instead. PROC MIXED: to 1000 is default) list models with TYPE=VC */

over Time line for the means). Specifically, & covariances) just a fixed variance instead. PROC MIXED: to 1000 is default) list models with TYPE=VC */ CLP 944 Example 4 page 1 Within-Personn Fluctuation in Symptom Severity over Time These data come from a study of weekly fluctuation in psoriasis severity. There was no intervention and no real reason

More information

Age 55 (x = 1) Age < 55 (x = 0)

Age 55 (x = 1) Age < 55 (x = 0) Logistic Regression with a Single Dichotomous Predictor EXAMPLE: Consider the data in the file CHDcsv Instead of examining the relationship between the continuous variable age and the presence or absence

More information

Testing Structural Equation Models: The Effect of Kurtosis

Testing Structural Equation Models: The Effect of Kurtosis Testing Structural Equation Models: The Effect of Kurtosis Tron Foss, Karl G Jöreskog & Ulf H Olsson Norwegian School of Management October 18, 2006 Abstract Various chi-square statistics are used for

More information

Introduction to Structural Equation Modeling: Issues and Practical Considerations

Introduction to Structural Equation Modeling: Issues and Practical Considerations An NCME Instructional Module on Introduction to Structural Equation Modeling: Issues and Practical Considerations Pui-Wa Lei and Qiong Wu, The Pennsylvania State University Structural equation modeling

More information

From Practical Data Analysis with JMP, Second Edition. Full book available for purchase here. About This Book... xiii About The Author...

From Practical Data Analysis with JMP, Second Edition. Full book available for purchase here. About This Book... xiii About The Author... From Practical Data Analysis with JMP, Second Edition. Full book available for purchase here. Contents About This Book... xiii About The Author... xxiii Chapter 1 Getting Started: Data Analysis with JMP...

More information

Confirmatory Factor Analysis

Confirmatory Factor Analysis Confirmatory Factor Analysis Latent Trait Measurement and Structural Equation Models Lecture #6 February 13, 2013 PSYC 948: Lecture #6 Today s Class An introduction to confirmatory factor analysis The

More information

MIXED MODELS FOR REPEATED (LONGITUDINAL) DATA PART 2 DAVID C. HOWELL 4/1/2010

MIXED MODELS FOR REPEATED (LONGITUDINAL) DATA PART 2 DAVID C. HOWELL 4/1/2010 MIXED MODELS FOR REPEATED (LONGITUDINAL) DATA PART 2 DAVID C. HOWELL 4/1/2010 Part 1 of this document can be found at http://www.uvm.edu/~dhowell/methods/supplements/mixed Models for Repeated Measures1.pdf

More information

Estimation of Curvilinear Effects in SEM. Rex B. Kline, September 2009

Estimation of Curvilinear Effects in SEM. Rex B. Kline, September 2009 Estimation of Curvilinear Effects in SEM Supplement to Principles and Practice of Structural Equation Modeling (3rd ed.) Rex B. Kline, September 009 Curvlinear Effects of Observed Variables Consider the

More information

Package sempower. March 27, 2018

Package sempower. March 27, 2018 Tye Package Title Power Analyses for SEM Version 1.0.0 Author Morten Moshagen Package sempower March 27, 2018 Maintainer Morten Moshagen Provides a-riori, ost-hoc, and comromise

More information

Subject-specific observed profiles of log(fev1) vs age First 50 subjects in Six Cities Study

Subject-specific observed profiles of log(fev1) vs age First 50 subjects in Six Cities Study Subject-specific observed profiles of log(fev1) vs age First 50 subjects in Six Cities Study 1.4 0.0-6 7 8 9 10 11 12 13 14 15 16 17 18 19 age Model 1: A simple broken stick model with knot at 14 fit with

More information

STAT 510 Final Exam Spring 2015

STAT 510 Final Exam Spring 2015 STAT 510 Final Exam Spring 2015 Instructions: The is a closed-notes, closed-book exam No calculator or electronic device of any kind may be used Use nothing but a pen or pencil Please write your name and

More information

Model Estimation Example

Model Estimation Example Ronald H. Heck 1 EDEP 606: Multivariate Methods (S2013) April 7, 2013 Model Estimation Example As we have moved through the course this semester, we have encountered the concept of model estimation. Discussions

More information

Factor Analysis & Structural Equation Models. CS185 Human Computer Interaction

Factor Analysis & Structural Equation Models. CS185 Human Computer Interaction Factor Analysis & Structural Equation Models CS185 Human Computer Interaction MoodPlay Recommender (Andjelkovic et al, UMAP 2016) Online system available here: http://ugallery.pythonanywhere.com/ 2 3 Structural

More information

Advanced Quantitative Data Analysis

Advanced Quantitative Data Analysis Chapter 24 Advanced Quantitative Data Analysis Daniel Muijs Doing Regression Analysis in SPSS When we want to do regression analysis in SPSS, we have to go through the following steps: 1 As usual, we choose

More information

A Threshold-Free Approach to the Study of the Structure of Binary Data

A Threshold-Free Approach to the Study of the Structure of Binary Data International Journal of Statistics and Probability; Vol. 2, No. 2; 2013 ISSN 1927-7032 E-ISSN 1927-7040 Published by Canadian Center of Science and Education A Threshold-Free Approach to the Study of

More information

Contents. Acknowledgments. xix

Contents. Acknowledgments. xix Table of Preface Acknowledgments page xv xix 1 Introduction 1 The Role of the Computer in Data Analysis 1 Statistics: Descriptive and Inferential 2 Variables and Constants 3 The Measurement of Variables

More information

ssh tap sas913, sas https://www.statlab.umd.edu/sasdoc/sashtml/onldoc.htm

ssh tap sas913, sas https://www.statlab.umd.edu/sasdoc/sashtml/onldoc.htm Kedem, STAT 430 SAS Examples: Logistic Regression ==================================== ssh abc@glue.umd.edu, tap sas913, sas https://www.statlab.umd.edu/sasdoc/sashtml/onldoc.htm a. Logistic regression.

More information