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Robustness in Confirmatory Factor Analysis

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Robustness in Confirmatory Factor Analysis the Effect of Sample Size, Degree of Non-Normality, Model, and Estimation Method on Accuracy of Estimation for Standard Errors
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Min, Youngkyoung
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Doctorate ( Ph.D.)
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University of Florida
Degree Disciplines:
Research and Evaluation Methodology
Educational Psychology
Committee Chair:
Algina, James J.
Committee Members:
Miller, M David
Leite, Walter
Garvan, Cynthia W.
Graduation Date:
8/9/2008

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Covariance ( jstor )
Estimation bias ( jstor )
Estimation methods ( jstor )
Kurtosis ( jstor )
Population estimates ( jstor )
Sample size ( jstor )
Standard error ( jstor )
Statistical discrepancies ( jstor )
Statistical estimation ( jstor )
Statistical models ( jstor )
Educational Psychology -- Dissertations, Academic -- UF
analysis, carlo, confirmatory, distribution, error, estimation, factor, gls, method, ml, model, monte, nonnormal, robust, sample, scale, setting, simulation, size, standard
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theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Research and Evaluation Methodology thesis, Ph.D.

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Abstract:
A Monte Carlo approach was employed to examine the effect of model type, sample size, and characteristic of distribution on the Maximum Likelihood (ML), Generalized Least Squares (GLS), Robust ML, and Robust GLS estimates of parameters? standard errors. The LISREL program was used for estimation, and the population covariance matrix and data were generated by using the SAS program. For each of four estimation methods (ML, GLS, Robust ML, and Robust GLS) the behavior of standard error ratio estimates was examined under each combination of four distributions, four sample sizes (200, 400, 800, and 1200), three CFA models, and two scale-setting methods (set by specifying factor variances equal to one and factor loadings equal to one). In addition, the bias of the parameter estimation procedures was investigated. The effects of four factors (estimation method, distribution, model, and sample size) on parameter estimates and standard error estimates were examined within each scale-setting method with Welch-James test and eta squared. Results for parameter estimates indicate that ML estimates were almost unbiased at all sample sizes and ML estimation had less bias than GLS estimation, although the differences were trivial for factor loadings and factor correlations. Sample size played a more critical role in GLS estimation than in ML estimation of residual variance and, as a result, larger between-method differences in bias were observed for estimates of residual variance. When the scale was set by specifying factor loadings equal to one, there were no important effects of the factors on the factor loading, factor variance, or factor covariance estimates. Results for standard error estimates indicate that Robust ML estimates were superior to the non-robust estimates in the bias of the standard error estimates for the non-normal distributions, and the standard error estimates were underestimated for the distribution with positive kurtosis and overestimated for the distribution with negative kurtosis. From the results, it can be concluded that ML estimation method should be adopted for a normal distribution regardless of sample size, model, and scale-setting method to obtain less biased estimates of parameters and standard errors, and Robust ML should be used for non-normal distributions to improve estimation of standard errors. However, Robust ML estimation works very well even for a normal distribution and some cases better than GLS. It was also found that robust estimation generally worked better than non-robust estimation for the non-normal distributions regardless of the sample size and the model type. When the distribution is non-normal, Robust GLS generally performs well, although Robust ML has less bias than Robust GLS. ( en )
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In the series University of Florida Digital Collections.
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Thesis (Ph.D.)--University of Florida, 2008.
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Adviser: Algina, James J.
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RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31
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by Youngkyoung Min.

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1 ROBUSTNESS IN CONFIRMATORY FACTOR ANALYSIS: THE EFFECT OF SAMPLE SIZE, DEGREE OF NON-NORMALITY, MODEL, AND ESTIMATION METHOD ON ACCURACY OF ESTIMATION FOR STANDARD ERRORS By YOUNGKYOUNG MIN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Youngkyoung Min

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3 To my Mom, Taesun Jeong for her endless love, support, encouragement, and prayer

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4 ACKNOWLEDGMENTS First of all, I would like to thank God and m y mother for every moment of my life and for providing the opportunity to gain this tremendous education. I am thankful to Dr. James Algina and Dr. David Miller for giving me the opportunity to study in Research and Evaluation Methodology Pr ogram, and for supporting me to finish my doctoral study. I owe much gratitu de to the faculty of Departme nt of Educational Psychology for his/her excellent lecture a nd thoughtful consideration. The completion of this dissertation would not be possible without the guidance of my dissertation committee. Each has helped me th roughout my dissertation in his/her own ways. I especially wish to thank Dr. James Algina, my supervisory committee chair and advisor, for his precious time, professional mentoring, and valuab le guidance during the last four years. I also wish to thank Dr. David Miller for his cons ideration and useful advice which made my confidence firm in the entire proc ess. I would like to thank Dr. Cynthia Garvan for her constant encouragement and challenging suggestions which made me stronger academically. She was also the committee chair of my thesis for M. S. in Statistics and encouraged me to pursue Ph. D. I also want to thank Dr. Walter Leite for his useful information on references and valuable suggestions which made this dissertation rich. I wish to give many thanks to Dr. Timothy Anderson, Associate Dean at the College of Engineering, for giving me the opportunity to wo rk at Engineering Education Center throughout my doctoral study, and for letting me obtain valuable research experiences in engineering college education field. I wish to thank Erica Hughes for her friendly tips a nd technical support. I would like to thank Dr. Mark Shermis, Ch air of Department of Educational Psychology, for making the departmental pro cess smooth and giving me practical tips for graduation. Lastly, I

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5 want to express my gratitude to Elaine Green and Linda Parsons, for th eir kind support and help on the official process for a graduate student during my doctoral study. Give Thanks to the LORD, for he is good; his love endures forever. Psalms 107:1

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF ABBREVIATIONS........................................................................................................ 11 ABSTRACT...................................................................................................................................12 CHAP TER 1 INTRODUCTION AND LITERATURE REVIEW..............................................................14 Introduction................................................................................................................... ..........14 Overview of Overall Robustness Studies in SEM.................................................................. 14 Covariance Structure Models a nd Estim ation Methods in SEM............................................ 16 Robustness against Non-normal Distribution......................................................................... 24 Skewness and Kurtosis in Univariate Distribution.......................................................... 25 Skewness and Kurtosis in Mu ltivariate Distributions .....................................................27 Robustness against Small Sample Size................................................................................... 29 Research Summary Characteristics........................................................................................30 Findings from Overall Past Robustness Studies.....................................................................32 Direction for the Present Study............................................................................................... 33 2 DESIGN OF MONTE CARLO SIMULATION STUDY...................................................... 45 Data Generation......................................................................................................................45 Fleishmans Power Transformation Method................................................................... 45 Vale and Maurelli Data Generation Method ................................................................... 46 Estimation Methods................................................................................................................47 Sample Sizes...........................................................................................................................47 Distributional Characteristics................................................................................................. 48 Model Characteristics.......................................................................................................... ...48 Analytical Model Characteristics........................................................................................... 49 Number of Replications......................................................................................................... .50 Research Summary Statistics..................................................................................................50 Ways to Summarize and Present Results................................................................................ 51 3 MONTE CARLO STUDY RESULTS................................................................................... 53 Preliminary ANOVA Tests..................................................................................................... 53 Welch-James Tests and Et a Squared Statistics ....................................................................... 55 Effects on Parameter Estimates....................................................................................... 59 Effects on Standard Error Ratio Estimates...................................................................... 60 Practical Problems in Estimation............................................................................................ 67

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7 4 SUMMARY AND CONCLUSIONS.....................................................................................85 Comparison of Findings with Previous Studies......................................................................85 Parameter Estim ates........................................................................................................ 85 Standard Error Estimates................................................................................................. 87 Brief Summary........................................................................................................................90 Concluding Remarks............................................................................................................. .91 APPENDIX: STANDARD ERROR RESULTS FOR FACTOR CORRELATIONS: SCALE SET BY SPECIFYING FACTOR VARIANCES EQUAL TO ONE. ...................................93 LIST OF REFERENCES...............................................................................................................97 BIOGRAPHICAL SKETCH.......................................................................................................103

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8 LIST OF TABLES Table page 1-1 Overview of previous robustness st u dies against sample size and non-normality............ 39 1-2 Estimation methods investigated in the past robustness studies ........................................ 40 1-3 When are ML, GLS, and ADF equivalent?....................................................................... 41 1-4 Degree of non-normality examined in the past robustness studies.................................... 42 1-5 Range of sample size examined in the past robustness studies ......................................... 43 1-6 Number of replications in the past robustness studies....................................................... 44 3-1 Frequency of significance F tests on param eter estimates per 16 combinations of distribution and sample size: f actor variances equal to one.............................................. 70 3-2 Frequency of significant F tests on param eter estimates per 16 combinations of distribution and sample size: f actor loadings equal to one................................................ 71 3-3 Mean parameter estimate for = 0.6: Model 3F3I, GLS, N = 200, factor variances equal to one, and (0, -1.15)................................................................................................72 3-4 Frequency of significant F tests on standard error estim ates per 16 combinations of distribution and sample size: f actor variances equal to one............................................... 72 3-5 Frequency of significant F tests on standard error estim ates per 16 combinations of distribution and sample size: f actor loadings equal to one................................................ 73 3-6 Mean standard error estimates for = 0.3: model 6F3I, Robust ML, N = 1200, factor variances equal to one, and (2, 6)............................................................................ 73 3-7 F statistics (degrees of freedom ) for WJ tests for effects on parameter estimates: scale set by specifying factor variances equal to one........................................................ 74 3-8 Eta squared for significant effects on param eter estimates: scale set by specifying factor variances equal to one.............................................................................................. 75 3-9 Marginal means by estimation for estimates of = 0.8....................................................75 3-10 Mean estimates of residual variance param eters by parameter value, estimation method, and sample size.................................................................................................... 75 3-11 F statistics (degrees of freedom ) for WJ tests for effects on parameter estimates: scale set by specifying factor loadings equal to one.......................................................... 76

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9 3-12 Eta squared for significant effects on param eter estimates: scale set by specifying factor loadings equal to one............................................................................................... 77 3-13 Grand mean estimates of pa ram eter by parameter value................................................... 77 3-14 F statistics (degrees of freedom ) for WJ tests for effects on standard error ratio estimates: scale set by specifying f actor variances equal to one....................................... 78 3-15 Eta squared for significant effects on standard error ratio es timates: scale set by specifying factor variances equal to one............................................................................ 79 3-16 Marginal means by sample size for s tandard error ratio estimates of = 0.6................... 79 3-17 Mean estimates of standard error ratio for factor loading param eters by parameter value, estimation method, and distribution........................................................................ 80 3-18 Mean estimates of standard error ratio for factor corre lation parameters, (0.3) by model, sample size, and distribution.................................................................................. 80 3-19 Means estimates of standard error ratio for factor corre lation parameter, (0.3) by estimation and distribution................................................................................................. 80 3-20 Marginal means by sample size for s tandard error ratio estimates of = 0.36............... 81 3-21 Means estimates of standard error ratio for residual variance param eter, (0.36) by model and distribution....................................................................................................... 81 3-22 Means estimates of standard error ratio for residual variance param eters by parameter value, estimation method, and distribution....................................................... 81 3-23 F statistics (degrees of freedom ) for WJ tests for effects on standard error ratio estimates: scale set by specifying f actor loadings equal to one......................................... 82 3-24 Eta squared for significant effects on standard error ratio es timates: scale set by specifying factor loadings equal to one............................................................................. 83 3-25 Mean estimates of standard error ratio for factor loading param eters by parameter value, estimation method, and distribution........................................................................ 83 3-26 Marginal means by sample size for s tandard error ratio estimates of (i, j) = 0.108....... 83 3-27 Mean estimates of standard error ratio fo r factor corre lation parameters by parameter value, estimation method, and distribution........................................................................ 84 3-28 Frequency of non-convergence (NC), improper estimates (IE), and non-positive definite Hessians (NP) .......................................................................................................84

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10 A-1 Mean standard errors, empi rica l standard errors, and standard error ratios by model, distribution, and sample size for ML estimation............................................................... 93 A-2 Mean standard errors, empi rica l standard errors, and standard error ratios by model, distribution, and sample size for GLS estimation.............................................................. 94 A-3 Mean standard errors, empi rica l standard errors, and standard error ratios by model, distribution, and sample size for Robust ML estimation...................................................95 A-4 Mean standard errors, empi rica l standard errors, and standard error ratios by model, distribution, and sample size for Robust GLS estimation.................................................. 96

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11 LIST OF ABBREVIATIONS ML Maximum Likelihood GLS Generalized Least Squares ULS Unweighted Least Squares DWLS Diagonally Weighted Least Squares ADF Asymptotically Distribution Free RML Robust Maximum Likelihood / Robust ML RGLS Robust Generalized Least Squares / Robust GLS Factor Loading Factor Correlation (i, i) Factor Variance (i, j) Factor Covariance / Factor inter-correlation Residual Variance SEM Structural Equation Modeling 3F3I 3 Factors with 3 Indicators per factor 3F6I 3 Factors with 6 Indicators per factor 6F3I 6 Factors with 3 Indicators per factor (0, 0) Standard Normal Distribution w ith zero skewness and zero kurtosis (0, -1.15) Non-normal Distribution (plat ykurtic) with zero skewness and equal negative kurtosis (-1.15) (0, 3) Non-normal Distribution (leptokur tic) with zero skewness and equal positive kurtosis (3) (2, 6) Non-normal Distribution with high equal skewness (2) and high equal kurtosis (6)

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12 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ROBUSTNESS IN CONFIRMATORY FACTOR ANALYSIS: THE EFFECT OF SAMPLE SIZE, DEGREE OF NON-NORMALITY, MODEL, AND ESTIMATION METHOD ON ACCURACY OF ESTIMATION FOR STANDARD ERRORS By Youngkyoung Min August 2008 Chair: James Algina Major: Research and Evaluation Methodology A Monte Carlo approach was employed to exam ine the effect of model type, sample size, and characteristic of distribution on the Maxi mum Likelihood (ML), Generalized Least Squares (GLS), Robust ML, and Robust GLS estimates of parameters standard errors. The LISREL program was used for estimation, and the populati on covariance matrix and data were generated by using the SAS program. For each of four es timation methods (ML, GLS, Robust ML, and Robust GLS) the behavior of standard e rror ratio estimates was examined under each combination of four distributions, four sample sizes (200, 400, 800, and 1200), three CFA models, and two scale-setting methods (set by specifying factor varian ces equal to one and factor loadings equal to one). In addition, the bias of the parameter estimation procedures was investigated. The effects of four factors (estimation method, distributi on, model, and sample size) on parameter estimates and standard erro r estimates were examined within each scalesetting method with Welch-James test and eta squared. Results for parameter estimates indicate that ML estimates were almost unbiased at all sample sizes and ML estimation had less bias than GLS estimation, although the differences were trivial for factor loadings a nd factor correlations. Sample size played a more critical role in

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13 GLS estimation than in ML estimation of residu al variance and, as a result, larger betweenmethod differences in bias were observed for estim ates of residual variance. When the scale was set by specifying factor loadings equal to one, th ere were no important effects of the factors on the factor loading, fact or variance, or factor covariance estimates. Results for standard error estimates indicate that Robust ML estimates were superior to the non-robust estimates in the bias of th e standard error estimates for the non-normal distributions, and the standard error estimates were underestimat ed for the distribution with positive kurtosis and overestimated for the distribution with negative kurtosis. From the results, it can be concluded that ML estimation method should be adopted for a normal distribution regardless of sample size, model, and scale-setting method to obtain less biased estimates of parameters and standard errors, and Robust ML should be used for nonnormal distributions to improve estimation of st andard errors. However, Robust ML estimation works very well even for a normal distribution a nd some cases better than GLS. It was also found that robust estimation generally worked better than non-robust estimation for the nonnormal distributions regardless of the sample si ze and the model type. When the distribution is non-normal, Robust GLS generally performs we ll, although Robust ML has less bias than Robust GLS.

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14 CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW Introduction Structural equation m odeling (SEM ) has been one of the importan t statistical approaches in the social sciences because it can be applied to non-experimental and experimental data and offers a convenient way to differentiate between observed variables and latent variables. SEM is often conducted under the assumption of multivaria te normality, which implies that all univariate distributions are normal, the joint distribution of any pair of the va riables is bivariate normal, and all bivariate scatter-plots are linear and homoscedastic (Kline, 2005). However, real data in social science are rarely normal. The viola tion of normality assumption can cause incorrect results in SEM. Checking the degree of non-normality of the data and the use of the methods that are less reliant on normality are necessary. Over th e past years, the effects of non-normality have been studied and different types of solutions have been proposed. Speci fically, it was recognized that the model, the estimation method, and data char acteristics such as sample size play critical roles under non-normality. Therefore, the present study used simulation methods to investigate the impact of sample size on the accuracy w ith which standard errors are estimated under varying degrees of non-normality, different estima tion methods, and different structural equation models which include latent variables. In th e present study, the effect s of model complexity, sample size, estimation method, scale-setting method, and degree of non-normality on the accuracy of estimated standard errors were investigated. Overview of Overall Robustness Studies in SEM As the term has come to be used in statistics, robustness refers to th e ability to withstand violations of theoretical assumptions (Boo msma, 1983; Harlow, 1985). A common procedure for studying robustness is to generate data sets a nd examine the behavior of research summary

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15 characteristics such as test-stati stics, standard errors, etc. This procedure is known as the Monte Carlo simulation method and has been used in mo st of previous robustness studies in SEM (e.g., Anderson & Gerbing, 1984; Bearden et al., 1982 ; Boomsma, 1983; Browne, 1984; Muthen & Kaplan, 1985, 1992). The issues of robustness against small sample size, distributional viol ations, analysis of correlation matrices, model misspecification, and non linear structural equations under different estimation methods have been investigated over the past decade. Hoogland (1999) set forth the following five robustness questions: Do the maximum likelihood (ML) and generalized least squares (GLS) estimators possess the asymptotic statistical properties predicted under normal theory when variables are nonnormally distributed? Does a small sample size cause problems, because the statistical properties of the parameter estimators, standa rd error estimators and goodness -of-fit test statistics are asymptotic properties? Does analysis of correlation ra ther than covariance matrices cause problems when a model is not scale invariant? Does model misspecification cause the incorrect results of analyses? Does ignoring the nonlinear relationships between latent variables cause the wrong solution? The problems of analyzing correlation matr ices, model misspecification, and nonlinear structure were excluded in the present study because covariance matrices were used, and the structural equation models we re specified to be linear. A review of past robustness studies was necessary to set up the purpose of the current study and the design of the Monte Carlo study. Tabl e 1-1 shows an overview of a collection of robustness studies for the effects of sample size and non-normality. (The numbers in the Paper Number column will be used in Tables 1-2, 14, 1-5, and 1-6 to identify the studies listed in Table 1-1.) Of the 47 studies, a total of 41 studies investigated the effect of sample size on

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16 robustness and a total of 34 st udied the effects of non-normality on robustness in SEM. Other robustness issues (the problem of analyzing co rrelation matrices, model misspecification, and non-linear structure) were excluded from Table 1-1 and the present study. Covariance Structure Models and Estimation Methods in SEM The funda mental hypothesis in c ovariance structure modeling is ),( where (k k) is the population c ovariance matrix of k observed variables,) ( (k k) is the population covariance matrix of k observed variables written as a hypothesized function of, and (t 1) is a vector of the model parameters. The sample estimator of the population covariance matrix in a sample of size N is )1/( NZZS, where Z is an ( N k) matrix of deviation scores of the observed variables and k is the number of the observed variables. A general formulation of a covariance struct ure model for confirmato ry factor analysis (CFA) with latent variables is as follows (Jreskog & Srbom 1996). Using LISREL notation, x where x is a ( k 1) vector of indicators (the obs erved or measured variables) of the m exogenous latent variables, is a ( k m ) matrix of the loadings of x on, and is a ( k 1) vector of measurement errors. It is assumed that thes and s are random variables with zero means, s are uncorrelated with s, and all observed variables are meas ured in deviations from their means. The measurement model represents the regression of x on and the element ij of is the partial regressi on coefficient of j in the regression of ix on 1 2 m The model implied covariance matrix for the x variables is defined as:

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17 ) (, where is the covariance matrix for and is the covariance matrix for. Given a specified model,(), the unknown parameters of are estimated so that the discrepancy between the sample implied covariance matrix ) ( and the sample covariance matrix, S is as small as possible given some criterion, where is the vector of parameter estimates. A discrepancy function )) (,( SFis needed to quantify the fit of a model to the sample data. This function should have the following properties: 1. ))(,( SF is a scalar, and )) (,( SF 0. 2. ))(,( SF = 0 if and only if ( ) = S 3. ))(,( SF is twice differentiable in ) ( and S Minimizing a discrepancy function with these properties leads to consistent estimators of when the model is correctly specified and some regularity conditions are satisfied (Browne, 1984; Hoogland & Boomsma, 1998). With these definitions and properties, Browne (1982, 1984) framed the discrepancy function approach into a weighted least square s (WLS) approach and demonstrated that all existing discrepancy functions we re special cases of the follo wing WLS discrepancy function: )),(())(()( sV s F where ) ,...,,,,()(31222111 kksssssvecSKs is a vector consisti ng of the nonduplicated elements of S, )( S vec is a vector of order k2 1 consisting of the columns of S strung under each other, 1(), KDDD where D is the duplication matrix which transforms s to (),vecS K is the generalized inverse of D which transforms )( S vec to s and V is a specific positive definite weight matrix and is defined differently for different discrepancy functions:

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18 Unweighted least squares (ULS): *IV ULS Generalized least squares (GLS): D DV )(11 GLS Maximum likelihood (ML): D DV )) () ((1 1 ML Asymptotically distribution free (ADF): 1 NNT NNTWV Diagona lly weighted least squares (DWLS):1 1][ NNT W DWLSdiagNNTW DV. In theses expressions,,...) 1,2,2,1,2,1(*diag is the symbol for a Kronecker product, and NNTW is the asymptotic covariance matrix of S estimated without assuming normality. DWLS can be formulated by using the diagonal elements of ,NTGLS WV but in this dissertation DWLS refers to estimates obtained using D WLSV defined above. As suggested by the expressions for the weight matrices for GLS and ML estimation, seve ral of the weight matrices are functions of population parameters and must, in practice, be estimated. The goal of estimation is to produce ) ( that is as similar as possible to the sample covariance matrix, S with similarity defined by the disc repancy function. The weight matrix, V in the discrepancy functions above, determines the estimation method chosen. Let U be the sampling covariance matrix of the non-redundant elements of Sand |)]([ The optimal choice for the weight matrix is 1 U and this matrix provides best generalized least squares estimates. For example, if the data are multivariate normal GLSV or M LV provide optimal weight matrices. Browne (1982) has shown that if the optimal weight matrix is used, the asymptotic sampling covariance matrix for the parameter estimates is11 1)()1( U N. For any other choice of the weight matrix th e asymptotic sampling covariance matrix is1 1 1))(()()1( V VUV V N. In addition, Browne (1982) has shown that the diagonal

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19 elements of 11 1)()1( U N are not smaller than th e diagonal elements of 1 1 1))(()()1( V VUV V N so that the standard errors of the best generalized least squares (BGLS) estimator cannot be larger than the standard errors when a sub-optimal weight matrix is used. An important aspect of covariance structure an alysis is the evaluation of the fit of the model. A typical statisti c for such evaluation is ) ()1( )1( FNFN which is the so called chi-square goodness of fit statistic (Satorra, 1990). The residual vector, )) ( ( SK contains the non-redundant residuals and al so provides information for th e fit of the model (Satorra, 1990). It may be noted that under the assumption of the asymptotic normality of the residual vector, ), KS ( WLS estimation amounts to a problem in the distribution of a quadratic form in normal variables about which much is known. In fact, if one considers obtaining the symmetric square root 1/2V of V in the WLS discrepancy function above and multiplying the resulting matrix into the vectors on either side, it is apparent with an optimal V one obtains a vector of independent variates. Thus, the WL S discrepancy function can be considered to represent the sum of squares of independent normal variables which is intimately related to the chi-square distribution. In fact, with such an optimal weight matrix, the WLS estimator is a minimum chi-square estimator, or minimum modi fied chi-square estimator when the weight matrix is estimated (Bentler, 1983). The matrix 2,NNT WK CK where C is a 22kk matrix with elements that are fourth order cumulants of x the 1 k random vector for the data with mean and covariance matrix (Browne & Shapiro, 1988). That is ).2()()()(}]))({(}))({([1 KKKK x x x x C cvevec cve vecE

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20 The matrix NNTNW1)1( is the asymptotic covariance matr ix of the non-redundant elements of the covariance matrix S and its elements will be finite assuming only that the variables have finite fourth order cumulants. Because ADF and DWLS are based on NNTW they do not make strong assumptions about the distribution of the data. ULS does not make any assumptions about the data but also uses less information about th e data than do ADF or DWLS. If the data are normal, C0 and NNTW simplifies to 2.NT WK K As GLS uses the inverse of NTW as its weight matrix, GLS is based on the normality assumption. ML estimates can be obtained by using two di fferent discrepancy functions. First ML estimates can be obtained by minimizing the following discrepancy function which is wellknown: k tr F ||log})({|)(|log)(1S S In addition ML estimates can be obtained by minimizing ))(())(()( sV s MLF. The ML weight matrix, MLV is derived as a function of elements of) ( This means that the ML weight matrix is effectively updated as the estimate of ) ( changes at each iteration in the estimation process. Both of these discrepancy func tions have a minimum at the same point in the parameter space, namely at the ML estimates, but the minimum value of the functions are not the same (Jreskog et al., 1996; Satorra, 1990). The first of the two functions is referred to as the ML discrepancy function and ) ()1( )1( FNFN is referred to as the minimum fit function chi square. The second function is referred to as th e normal theory WLS discrepancy function and ) ()1( )1( FNFN is referred to as the normal theory weighted least squares chi square. ML estimates assume the data are drawn from a multivariate normal distribution.

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21 If an estimate of the optimal weight matrix is used, for example when the data are multivariate normal and GLS is used, then the sampling covariance matrix for is consistently estimated by 1 1)()1( V N where is the Jacobian matrix. However, if the weight matrix is not correct for the distribution of the data 1 1)()1( V N may not be a consistent estimator of sampling covariance matrix for Browne (1982) has shown that 1 1 1)( )()1( V V VW V NNTN is consistent even when the weight matrix is not correct for the distribution of the data. Using this expression, replacing V by D DV )) () ((1 1 ML and NNTW by a consistent estimator, to calculate standard errors of the ML estimates is referred to as Robust ML. The expression 1 1 1)( )()1( V V VW V NNTN can also be used with other weight matrices and will provide a consistent estimator of the sampling covariance matrix for the parameters. Replacing V by D WLSV ,GLSV and ULSV to calculate standard erro rs of the DWLS, GLS, and ULS estimates, respectively, are called R obust DWLS, Robust GLS, and Robust ULS, respectively. In particular applyi ng this procedure to the DWLS estimator may be attractive. The weight matrix in DWLS is N NTW diagonal. Thus, like ADF it is not based on the normality assumption, but unlike ADF will not be affected by sampling errors in the off-diagonal elements of an estimator of N NTW which is a 1212kkkk matrix and is likely to have substantial sampling error. Browne and Shapiro (1987) have shown that when the weight matrix for ML or GLS is used but the weight matrix is not optimal, under certain conditions1 1)()1( V MLN and 1 1)()1( V GLSN where 11,GLS VD D provides consistent estimates of the

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22 standard errors of the estim ates of the elements of but not of the other parameters, even when the random vectors in the model are not normally di stributed. For the factor analysis model the required conditions are that (a) the various latent random vectors for a model are independent, not merely uncorrelated, (b) uncorrelated el ements within a latent random vector are independent, not merely uncorrelated, and (c) other than constraints that the off-diagonal elements of a covariance matrix for a random v ector are zero, the covari ance matrices must be unconstrained. For the factor analysis model the elements of the vectors which are specified to be uncorrelated in the CFA m odel, must be mutually statistically independent. Similarly the elements of the vectors which are specified in the CFA model to be uncorrelated with the elements of must be statistically inde pendent of the elements of In addition, with the exception of the constraints th at off-diagonal elements of are zero and of any constraints that off-diagonal elements of are zero, the elements of and must be unconstrained. The assumptions in Browne and Shapiro imply that Cis a function of the cumulant matrix for the random vectors in the model and of the matrix If the assumptions are not correct standard errors should be calculated by using 1 1 1)( )()1( V V VW V NNTN Table 1-2 lists the estimation methods investigated in the past robustness studies identified in the Table 1-1. Of the 47 studies ML was investigated in a total of 43 studies, GLS in a total of 16 studies, and ADF in a total of 22 studies. Es timation methods other than ML, GLS, ULS, DWLS, their robust versions, and ADF were not considered in preparing Table 1-2. The most popular techniques for estimating th e parameters in SEM are ML and GLS and there are three major assumptions for ML and GLS (Boomsma & Hoogland, 2001):

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23 The sample observations are independently distributed. The sample observations are multivariate normally distributed. The hypothesized model is approximately correct. As was mentioned above, real data in social science are ra rely normally distributed. The violation of normality assumption raises a robustness question in terms of distributional violation. However, as sample size increases, the distribution of the estimator approximates a normal distribution, which is why many research ers have investigated the effect of nonnormality in SEM with different sample sizes to determine the minimum required sample size for valid parameter estimates. The restrictive characteristic of the normality assumption motivated the development of the WLS procedure, an asymptotically distribu tion-free (ADF) method. This method does not assume a specific distribution and can produce resu lts which are valid under a wide variety of distributions of the data. Howeve r, these ADF methods face some pr actical problems in that they are computationally expensive and they lack robu stness against small to moderate sample sizes (Satorra, 1990). That is, ADF methods need a su fficiently large sample size. Thus the normality assumption still plays a major role in the pr actice of structural equation modeling. Several issues in regard to robustness of estimation methods against non-normality in SEM were introduced and reviewed and by Satorra (1990). He summarized several estimation methods (ML, ULS, DWLS, and ADF) and asympto tic robustness of normal theory inferences. Additionally, theore tical and empirical robustnes s to violation of assump tions were explained, a distinction was made between es timation methods that are eith er correctly or incorrectly specified for the distribution of data being analyzed, and a co mparison of ML, ADF, and Robust ML was reported using a real data example about teacher stress (Bentler & Dudgeon, 1996).

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24 Moreover, there are factors affecting the eval uation and modification of a model such as nonnormality, missing data, specification error, sensitivity to sample size, etc. In regard to these aspects, general guidelines for covariance struct ural equation modeling ha ve been presented over the past decades (Kaplan, 1990; MacCallum, 1990; Ullman, 2006). Robustness against Nonnormal Distribution The param eter estimates are derived from info rmation in the sample covariance matrix and the weight matrix. When the obs ervations are continuous non-nor mal, the information in the sample covariance matrix or the weight matrix or both may be incorrect. Consequently, estimates based on the sample covariance matrix and the wei ght matrix may also be incorrect. The present study examined non-normality of the observed co ntinuous variables. The variation in the measured variables is completely summarized by the sample covariances only when multivariate normality is present. If multivariate normality is violated, the variation of the measured variables will not be completely summarized by the sample covariances, so information from higher order moments is lost. In this situat ion, the parameter estimates do remain unbiased and consistent as sample size grows larger, but they are no longer efficient. These re sults suggest that theoretically important problems will occur with normal theo ry estimators such as ML and GLS when the observed variables do not have a multivariate norm al distribution. The im pacts of non-normality are that chi-square statistics, standard errors, or tests of all parameter estimates can be biased (West et al., 1995). Most of previous studies of violation of the normality assumption are for ML, GLS, and ADF (e.g., Muthen & Kaplan, 1985, 1992; B oomsma, 1983; Hoogland, 1999; Boomsma & Hoogland, 2001; Olsson et al., 2000; Lei & Lomax, 2005). Table 1-3 provides a simple comparison of ML, GLS, and ADF, taken from Olsson, Foss, Troye, a nd Howell (2000). As shown in Table 1-3, when the models are incorrectly specified and the data are not multivariate

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25 normal, the methods should give different results. With multivariate normal data but a misspecified model, ADF and GLS will be equivalent (Olsson et al., 2000). If the variables are highly non-normal, it is still an open question whether to use ML, GLS, or ADF. Previous studies have not given a clear-cut answer as to when it is necessary to use which estimation method and it is possible that standard erro rs produced by ML, GLS, ULS, DWLS may be underestimated when the observed variables deviate far from normality (LISREL 8 Users Reference Guide). Table 1-4 shows the degree of non-normality investigated in the past robustness studies. The present study examined robustness against violations of the assumption of normality using Monte Carlo methods. The degree of nonnormality can be specified by skewness and kurtosis. A brief review of skewness and kurtosis is presented below before discussing the range of non-normality utili zed in this study. Skewness and Kurtosis in U nivariate Distribution Univariate skewness can be viewed as how much a distribution de parts from symmetry, and univariate kurtosis has been described the extent to which the height of the probability density differs from that of the normal density curv e, that is, kurtosis measures the peakedness or flatness of the probability dens ity function (Casella & Berger, 2002; Harlow, 1985; West et al., 1995). Negative skewness indicates a distribution with an elongated left-hand tail and positive skewness indicates a distribution w ith an elongated right-hand tail relative to the symmetrical normal distribution. Zero skewness indicates symmetry around the mean. Negative kurtosis indicates flatness and short tails relative to a normal distribution, whereas positive kurtosis indicates peakedness and long tails relative to a normal curve (Bentler & Yuan, 1999; Harlow, 1985; Olsson et al., 2000; West et al., 1995).

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26 Measures of skewness and kurtosis can be defined by using central moments or by using cumulants. The nth central moment of X,n is ])[(n nXE or dxxfxX n n)()( where the first non-central moment is the mean: EX 1 and )( xfX is the probability density function of a con tinuous random variable, X The second central moment is the variance: 22 2[()](). E XVarXEXEX Kendall, Stuart, and Ord (1987) pr esented several measures of sk ewness and kurtosi s in terms of the moments of a distribution. Skew ness and kurtosis can be defined as 1 and 2 respectively, where 3 2 2 3 1 and 4 2 2 2. In the univariate normal case, where the variables are assumed to have symmetrical, bell-shaped distributions, 1 = 0 and 2 = 3. Measures of skewness and kurtosis can also be defined by using cumulants. Formally, the cumulants 1 2 n are defined by the identity in t !/)!/ exp(0 1ntntn n n n n n

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27 and it should be observed that there is no 0 (Kendall et al., 1987). The cumulant generating function is simply the logarithm of the moment generating function. The first order cumulant is simply the mean (expected value); the second or der and third order cumulants are respectively the second and third central moments; the hi gher order cumulants are neither moments nor central moments, but rather more complicated polynomial functions of the moments. For example the fourth order cumulant is 2 4423. Kendall, Stuart, and Ord (1987) used cumulants to also define alte rnative measures with the of skewness and kurtosis, respectively 33 1 3/23/2 22 44 22 22 2233 where 1 = 0, and 2 = 0 for univariate normal distributions. Skewness and Kurtosis in Mu ltivariate Distributions Examinations of the skewness and kurtosis of the univariate distributi ons provide only an initial check on multivariate normality. If any of the observed variables deviate from univariate normality, the multivariate distribution cannot be multinormal (West et al., 1995). While univariate measures of skewness and kurtosis are informative regarding the marginal distribution of a variable, it is also of interest to have info rmation on the joint distribu tion of a set of variables (Halrow, 1985). As a result, it is important to examine multivariate measures of skewness and kurtosis developed by Mardia (1970, 1974). The Mardia measures are functions of the third order moments and the fourth order moments, which possess approximate standard normal

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28 distributions permitting tests of multivariate skewness and multivariate kurtosis (Harlow, 1985; Mardia, 1970, 1974; West et al., 1995). Let ) ,...,(1 pXXX be a random vector with mean vector ) ,...,(1 p and covariance matrix ) (rs Mardia (1970, 1974) defined the measures of multivariate skewness and kurtosis as 3 1 ,1)}(){( Y X Ep, and 3 1 ,2)}(){( X X Ep, where X and Y are independent and identical random vect ors. Mardia (1974) also suggested the alternative expression for these measures in terms of cumulants and the expressions corresponding to p ,1 and p ,2 are )( 111111 ,1 tsrrstttssrr p and rstuturs p 1111 ,2 where )...( ...1 1 s srr ii denote the cumulant of order ) ,...,(1 sii for the random variable ) ,...,(1 sr rXX where r1, r2, rs are s integers taking values 1, 2, p and ) ()(1)( 11 rs rs. The relations between p ,1 and p ,1 and between p ,2 andp ,2 (Mardia, 1974; Kendall et al., 1987) are p p ,1,1 and )2(,2,2 ppp p

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29 In the multivariate normal case, if Xi has a normal distribution then 1,()i = 0 and2,()i = 0 and if X has a p-variate normal distribution p ,1 = 0 and also all fourth order cumulants are zero with the result that p ,2 = 0 (Browne, 1982). Robustness against Small Sample Size In SEM or any other procedure for fitting mode ls, inferences are made from observed data to the model believed to be generating the observa tions. These inferences are dependent in large part on the degree to which the information availa ble in a sample mirrors the information in the complete population. This depends on the obtained sample size. To the extent that samples are large, more information is available and more confidence can be expressed for the model as a reflection of the population process. Thus, sample size has always been an issue in SEM. To get the correct answer to the question: What is the minimum required sample size for each combination of estimation methods, distributiona l characteristics, and model characteristics? many studies have been conducted as shown in the list of the past robustn ess studies in Table 11. The range of sample sizes examined in the pa st robustness studies is shown in Table 1-5. The typical requirements for a covariance stru cture statistic to be trustworthy under the null hypothesis )( are that the sample observations are independently distributed and multivariate normally distributed in addition to identification of parameters. Identification and independence were assumed and not considered in the present study because they can often be arranged by design. Generally, real data do not meet the assumption of multivariate normality, so sample size turns out to be critic al because all of the goodness of f it statistics and standard errors used in covariance structure an alysis are asymptotic based on the assumption that sample size becomes arbitrarily large. Since this situation can rarely be obtained, it becomes important to evaluate how large the sample size must be in pr actice for the theory to work reasonably well.

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30 Different data and discrepancy functions have different robustness prope rties with respect to sample size. Basically, sample size requirement s increase as data become more non-normal, models become larger, and assumption-free disc repancy functions are used (Bentler & Dudgeon, 1996; Chou et al., 1991; Hu et al., 1992; Muthen &Kaplan, 1 992; West et al., 1995; Yung & Bentler, 1994). Sample size also affects the likelihood of non-convergence and improper solution. Nonconvergence occurs when a minimum of the discre pancy function cannot be obtained. Improper solutions refer to estimates that are outside of their proper range, for example estimated variances that are less than zero. The like lihood of non-convergence decreases with larger sample size; N >200 is generally safer. Non-convergence rates also decrease with larger factor loadings and larger k/ m ratio (Boomsma & Hoogland, 2001; Marsh et al., 1998), where k/ m is ratio of the number of observed variables to the number of common factors. The likelihood of improper solutions is reduced with a larger k/ m ratio and increased N (Boomsma & Hoogland, 2001; Marsh et al., 1998). Thus sample size plays a crucial role in robus tness, non-convergence, and improper solutions. Research Summary Characteristics Research summary characteristics determine how the quality of the simulation results is assessed. The following research summary statistic s have been used in the previous robustness studies of the Table 1-1. Bias of parameter estimates Bias of standard error estimates Standard deviation of parameter estimates Percentage of replications that lead to non-convergence

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31 Percentage of convergent replicatio ns that give improper solutions Rejection rate, mean, and standard de viation of a chi-square statistic p -value of the Kolmogorov-Smirnov test fo r a chi-square distribution Statistics The relative bias of paramete r estimators is defined as i i i iBias ) (, where i is the population value of the ith parameter (0 i ) and i is the mean of the estimates for the ith parameter across the total number of replications. The mean absolute relative bias for parameter estimation is t i iBias t1) ( 1 where t is the number of parameters in the model. According to Boomsma and Hoogland (2001) the mean absolute relative bias for parameter estimation should be less than 0.025. The relative bias of estimators for the standard error of parameter estimates i is defined as ) ( ) () ( )) ((i i i isd sdse seBias where ) (isd is the standard deviation of the estimates for parameter i and ) (ise is the mean of the standard error estimates regarding parameter i across the total number of replications. The mean absolute relative bias for standard error estimation is t i iseBias t1)) (( 1,

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32 which is considered to be accep table, if it is smaller than 0.05 (Boomsma & Hoogland, 2001; Harlow, 1985; Hoogland, 1999). Specially, standard errors of parameter estimates are important in many applications to judge the significance of the parameter estima tes. LISREL gives an estimated asymptotic standard error for each parameter. It is well known that these asymptotic standard errors for ML may be incorrect when the observed variable s are not multivariate normal (Browne, 1984; Jonsson, 1997). Findings from Overall Past Robustness Studies Findings from Boomsma (1983) and Muthen a nd Kaplan (1985) sugge st that estimated standard errors did not show bias when using ML, GLS, and ADF with approximately normal data. In non-normal samples, there is some evidence of negative bias in estimated standard errors when using ML with continuous data (Browne, 1984; Tanaka, 1984) as well as with ADF in sample sizes of 100 (Tanaka, 1984). In studies w ith 400 or more subjects, estimated standard errors also showed some bias, relative to empirical standard errors, with non-normal samples using ML with categorical data (Boomsma, 1983; Muthen & Kaplan, 1985) and ADF with categorical data (Mut hen & Kaplan, 1984). Hoogland summarized (1999) his conclusions ab out parameter estimators in his research with the following points: An important finding is that th e ML estimator of the model pa rameters (5 factors with 3 indicators per factor, and 4 f actors with 3 indicators per fa ctor) is almost unbiased when the sample size is at least 200. In the case of a small sample the GLS paramete r estimator has a much larger bias than the ML parameter estimator when the model ha s at least twelve obs erved variables. The bias of the ADF parameter estimator increases when the kurtosis increases. With positive kurtosis the bias of the ADF parame ter estimator is larger than that of the GLS parameter estimator.

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33 Hoogland made (1999) the followi ng points about the standard error estimators based on the results of his research and the previous studies: The ML and GLS estimators of the standard erro rs are biased when the average kurtosis of the observed variables de viates from zero. The standard errors are underestimated in th e case of a positive average kurtosis and overestimated in the case of negative average kurtosis. The ADF estimation method results in a large un derestimation of the standard errors when the sample size is small relative to the num ber of observed variab les in the model. The robust ML standard error estimator has a smaller bias than the other standard error estimations when the average kurtosis is at l east 2.0 and the sample size is at least 400. From the past robustness studies, th e following points can be set forth: The sample sizes 200 and 400 were often investigated and were minimum sample sizes, though it depends on the models investigated. If the variables are highly non-normal it is st ill an open question whether to use ML, GLS, ADF, or other methods in regard to bias of standard errors. Robust ML standard errors were rarely inves tigated for the effect of sample size and nonnormality compared with robustness studies in ML. DWLS estimation method was not examined for continuous non-nor mal distribution. Standard errors for Robust DWLS, Robust GLS, and Robust ULS were not investigated in previous studies, though GLS and ML estimatio n methods have quite often been studied. The numbers of replications, 100, 200, and 300 have often been used in previous robustness studies (Table 1-6). The number of replications is chosen to be reasonable because it is a trade off between precision a nd the amount of information to be handled (Hoogland, 1999). Effects of different scale setting methods on st andard error estimates in robust procedures were not investigated. Direction for the Present Study The present study examined the effect of sa mple size on the accuracy of estimation of standard errors under varying degrees of non-norma lity with four estimation methods (ML, GLS, Robust ML, and Robust GLS) and three CFA mo dels. The reasons ML, GLS, Robust ML, and

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34 Robust GLS were chosen as estimation methods are that standard errors of Robust ML and Robust GLS have not been very thoroughly investigated for effect of sample size, non-normality, and scale-setting method. One of the main purpos es of the present study is to answer the question: Which estimation method is better to get asymptotically correct st andard errors? A review of the previous studi es of Robust ML standard erro rs provides a clearer idea for the simulation design of the present study. Chou et al. (1991) investigat ed the effect of nonnormality on standard errors varying estimatio n methods (ML, Robust ML, and ADF) with two different versions of the CFA model, a sample size of 400, and 100 replications. For nonnormality condition, they chose four non-normal conditions: (a) symmetric with equal negative kurtosis, (b) symmetric with unequal kurtosis, (c ) unequal negative skewness with zero kurtosis, and (d) unequal skewness with unequal kurtosis to study the behavior of the robust standard errors. For the CFA model with two factors and three indicators per factor, there were two different versions. The first version contai ned 13 parameters, six factor loadings, six measurement error variances, and one factor co variance. The second version only included the measurement error variances and the factor covari ance as free parameters while fixing the factor loadings at the true values (C hou et al., 1991). In case of the first version of the model, for nonnormality conditions (a) and (c), all three types of estimated standard errors were very similar, mean ADF and robust standard errors were closer to the expected values. Under condition (b) and (d), all three types of estimated standard e rrors were negatively bias ed with robust standard errors performing slightly better than ADF and ML. Similar conclusions were drawn from the results for the second version of the CFA model. Robust stan dard errors provided better estimates than both ML and ADF, although some robus t standard errors were not as close to the

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35 expected values for the second version of the mo del as they were for the first version of the model. Finch et al. (1997) studied the effect of sa mple size and non-normality on the estimation of mediated effects in latent variable models. They investigated the mediated effects varying the structural regression coefficients in a model with three factors with three items per factor and concluded that the magnitude of the observed bias for estimating standard errors varied little across differing ratios of direct to indirect effects for ML, Robust ML, and ADF. First, ML, ADF, and Robust ML standard errors were exam ined to determine the range of non-normality conditions under which these standard errors ar e accurate, varying sample size (150, 250, 500, and 1000), the population values for the hybrid model parameter, and the degree of nonnormality (normal [0,0], moderate non-normality [2,7], and extreme non-normality [3, 21]) with 200 replications. The partially mediated struct ural model included th ree factors and three indicators per factor and had four different mediated effect size s. For Robust ML, weaker effects of non-normality on the standard e rrors were observed. With regard to the robust estimates of the standard error of the indirect effect, there wa s minimal bias under normality, and bias increased under severe non-normality. Weak effects of samples size were also observed with some decrease in relative bias associated with la rger sample sizes. Under non-normality, the Robust ML standard errors performed much better at all sample sizes. For all three methods of estimating standard errors, the magnitude of the observed bias varied li ttle across differing ratios of direct to indirect effects. The pattern of bias in the standard erro rs of direct and indirect effects was also not influenced by varia tion in the population values of the factor loadings. In the second study, they extended the generality of the findi ngs by examining bias under conditions in which the degree of non-normality differed across observed (manifest) variables. The population values

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36 of skewness and kurtosis were specified in th e three distributional c onditions. In the first distributional condition, the nine measured variab les were mildly to moderately non-normal; in the second the nine manifest variables were mode rately to severely non-n ormal; and in the third the measured indicators of the final outcome c onstruct were severely non-normal, whereas the other six variables were normally distributed. They adopted one specific latent variable model for the second study with 400 replications and the sa me conditions for sample size that they used in their first study. Under the first distributiona l condition, there were no appreciable effects of sample size on the estimated standard errors of the indirect effect using ML or Robust ML. Under the second distributional condition, Robust ML estimated standard errors of the structural coefficients showed a general tendency to beco me more accurate with increasing sample size. Under the third distributional condition, there we re large effects of non-normality on the ML and Robust ML standard errors of the latent variab le coefficients. The Robust ML standard errors provided more accurate estimates of sampling variability than the normal theory standard errors as non-normality increased. The Robust ML standard errors were also more accurate than the ADF standard errors under all di stributional conditions at the sm aller sample sizes (i.e., 150 or 250). Hoogland (1999) investigated the effect of four factors on rela tive bias of standard error estimates: sample size, size of factor loadings, model complexity, and number of indicators per factor. Model complexity was varied as follows: four factors with three items, four factors with four items, and five factors with three items pe r factor, and one hybrid model with five factors and three items per factor. For N = 200, the mean absolute relative bias of the Robust ML standard error estimator was larger for each parameter type when the indicators were poor measures of the latent variables. For N = 400, the mean absolute relative bias of the Robust ML

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37 standard errors was also larger for smaller factor loadings. For N 800, the effect of the size of the factor loadings on the mean absolute relative bias of the Robust ML standard error estimator was mostly small. In case of model complexity, th ere was hardly an effect on the mean absolute relative bias of the Robust ML standard errors as the m odel complexity increases for N 200. The mean absolute relative bias of the Robust ML standard errors decreased somewhat as the number of indicators per factor increased when the sample size was small. The difference between the CFA model and the hybrid model was negligible for the mean absolute relative bias of the Robust ML standard errors. According to the previous studies of Robust ML (Chou et al., 1991; Finch et al., 1997; Hoogland et al., 1999), Robust ML generally showed be tter performance on standard errors rather than other estimation methods investig ated over all sample sizes and degrees of nonnormality. For Robust GLS, no Monte Carlo study has been conducted to investigate the effect of sample size on accuracy of estimated standard erro r. Thus, a Monte Carlo study of the effect of sample size on accuracy of estimated standard errors under varying degrees of non-normality with three CFA models using ML, GLS, Robust ML, and Robust GLS will provide more precise guidelines in regard to sample size in SEM fo r a non-normally distributed data and a complex model. The scale of measurement for a factor can be changed without affecting the fit of model to the data, which is called scale indeterminacy. The data cannot determine the values of the parameters due to scale indeterminacy and therefore this is an identifi cation problem. Arbitrary restrictions are required to rem ove scale indeterminacy. A parameters standard error is sensitive to how the model is identified (i.e., how scale is set). The different scale-setting methods to

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38 identify a model may yield different standard errors and reach differe nt conclusion about a parameters significance although eq uivalent models on the same data are tested (Gonzalez & Griffin, 2001). There are two common methods to remove scale indeterminacy: 1) Factor variances are set equal to one, and 2) Factor lo adings are set equal to one. Ch anging the method for setting the scale of a factor multiplies the factor by a constant. Estimates of parameters related to the factor are affected as one would expect for a scale change. For exampl e if the method of setting the scale multiplies the factor by k, then the factor loading is multiplied by 1. k However, the standard errors of the factor loading are not simply multiplied by 1. k As a result it is possible that the method for setting the scales may affect the accuracy of estimating of standard errors for factor loadings and this questi on has not been investigated in the previous robust studies. Note that the scale-setting method does not affect the si ze of the residual variances at all, whereas the scale-setting method affects the size of the f actor loading, factor variances, and factor covariances.

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39 Table 1-1. Overview of previous robustne ss studies against sample size and non-normality Paper Number Author(s) Year Sample size Non-normality 1 Anderson & Gerbin g 1984O 2 Andreassen Lorentzen & Olsson2006 O 3 Babakus Fer g uson & Joresko g 1987O O 4 Baldwin 1986O 5 Beardeu Sharma & Teel 1982O 6 Beauducel & Herzber g 2006O 7 Benson & Fleishman 1994O O 8 Bentler & Yuan 1999O 9 Boomsma 1983O O 10 Brown 1990 O 11 Browne 1984 O 12 Chou Bentler & Satorra 1991O O 13 Curran West & Finch 1996O O 14 Dolan 1994O O 15 Ethin g ton 1987 O 16 Fan Thom p son & Wan g 1999O 17 Finch J. F. 1992O O 18 Finch West & Mackinnon 1997O O 19 Gallini & Mandeville 1984O 20 Gerbin g & Anderson 1985O 21 Harlow 1985O O 22 Hau & Marsh 2004O O 23 Henl y 1993O O 24 Hoo g land 1999O O 25 Hu Bentler & Kano 1992O O 26 Huba & Harlow 1987 O 27 Jaccard & Wan 1995O O 28 Jackson 2001O 29 Ka p lan 1989O 30 Lee Poon & Bentle r 1995O O 31 Lei & Lomax 2005O O 32 Muthen & Ka p lan 1985 O 33 Muthen & Ka p lan 1992O O 34 Nevitt & Hancock 2000O O 35 Nevitt & Hancock 2001O O 36 Nevitt & Hancock 2004O O 37 Olsson Foss Tro y e & Howell 2000O O 38 Pin g 1995O 39 Potthas t 1993O O 40 Redd y 1992O 41 Sharma Durvasula & Dillon 1989O O 42 Sivo Fan Witta & Willse 2006O O 43 Tanaka 1984O O 44 Tanaka 1987O 45 Yan g Jonsson 1997O O 46 Yuan & Bentle r 1998O O 47 Yun g & Bentle r 1994O O

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40 Table 1-2. Estimation methods investig ated in the past robustness studies Pa p er numbe r Estimation methods 1 ML 2 ML GLS ADF 3 ML 4 ML 5 ML 6 ML DWLS* 7 ML ADF 8 ML 9 ML 10 ML 11 ML ADF 12 ML ADF Robust ML 13 ML ADF 14 ML ADF GLS 15 ML ULS 16 ML GLS 17 ML 18 ML ADF Robust ML 19 ML 20 ML 21 ML ADF 22 ML ADF 23 ML GLS ADF 24 ML GLS ADF Robust ML 25 ML GLS ADF 26 ML GLS ADF ULS 27 ML GLS ADF 28 ML 29 ML GLS 30 GLS 31 ML GLS 32 ML GLS ADF 33 GLS ADF 34 ML 35 ML 36 ML ADF 37 ML GLS ADF 38 ML ADF 41 ML GLS 43 ML ADF 44 ML 45 ML ADF 46 ADF 47 ML GLS ADF In this article, DWLS means WLSMV (W LS means and variance adjusted) which is weighted least square parameter estimates us ing a diagonal weight matrix with standard errors and meanand varianceadjusted chi-square test statistic that use a full weight matrix.

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41 Table 1-3. When are ML, GLS, and ADF equivalent? Model/Distribution Normal Non-normal Correct model ML GLS ADF ML GLS asymptotically equivalent asymptotically equivalent Misspecified model GLS ADF No equivalence asymptotically equivalent

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42 Table 1-4. Degree of non-normality exam ined in the past robustness studies Paper number Degree of non-normality (Skewness, Kurtosis) 2 Non-normal real data 3 Combinations of skewness = 0.0, 0.5, 1.5 7 (1.0, 2.0), (2.0, 6.0) 9 Various combinations of skewness and kurtosis 10 (0.0, 0.0), (0.25, 3.75), (1.0, 3.75), (1.75, 3.75) 11 Multivariate normal distribution vs. rescaled multivariate chi-square distribution with 2 degree of fr eedom and all marginal relative kurtosis coefficients equal to 3.0. 12 Various combinations of skewness and kurtosis 13 (0.0, 0.0), (2.0, 7.0), (3.0, 21.0) 14 (0.0, 3.0), (0.0, 1.0) 15 Combinations of skewness = 0.0, 1.5, -1.5 17 (0.0, 0.0), (2.0, 7.0), (3.0, 21.0) 18 (0.0, 0.0), (2.0, 7.0), (3.0, 21.0) 19 Possible combinations with sk ewness = 0.0, 1.0, 2.0 and kurtosis = -1.0, 0, 1.0, 3.0, 6.0 with the restriction of (skewness)2 < 0.629576(kurtosis) + 0.717247 21 13 combinations of skewness and kurtosis 22 (0.0, 0.0), (0.5, 0.5), (1.0, 1.5), (1 .5, 3.25), Balanced ( 0.5, 0.5) 23 Multivariate normal, elliptical, non-elliptical 24 11 combinations of skewness and kurtosis 25 7 conditions of the range of kurtoses -0.010 ~ 0.010, -0.502 ~ 3.098, -0.502 ~ 3.908, -0.262 ~ 0.989 4.658 ~ 6.827, 4.635 ~ 9.659, 3.930 ~ 20.013 26 Non-normal real data 30 Continuous and polytomous variable 31 (0.0, 0.0), (0.3~0.4, 1.0), (0.7, >3.5) 33 (0.0, 0.0), (-0.74, -0.33), (-1.22, 0.85), (-2.03, 2.90), (0.0, 2.79), (0.0, -1.3) 34 (0.0, 0.0), (2.0, 7.0), (3.0, 21.0) 35 (0.0, 0.0), (2.0, 7.0), (3.0, 21.0) 36 (0.0, 0.0), (0.0, 6.0), (3.0, 21.0) 37 11 different levels of kurtosis in the latent variables ranging from mild (-1.2) to severe peakedness (+25.45) 39 Negative kurtosis(1.9, -1.12), zero skewness and kurtosis(0.0, 0.0), positive kurtosis(0.0, 2.79), high skewness and kurtosis(2.52, 5.8) 41 Combinations of skewness = -1.0, 0.0, 1.0 and kurtosis = 2.0, 4.0, 6.0 43 Zero skewness and various kurtosis 45 After data generation and estima tion procedure, checking the degree of non-normality (kurtosis and skewness with p-value)

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43 Table 1-5. Range of sample size exam ined in the past robustness studies Paper number Sample sizes 1 50, 75, 100, 150, 300 2 1042 3 100, 500 4 100, 200 5 25, 50, 75, 100, 500, 1000, 2500, 5000, 10000 6 250, 500, 750, 1000 7 200, 400 8 60, 70, 80, 90, 100, 110, 120 9 25, 50, 100, 200, 400, 800 10 500 11 500 12 200, 400 13 100, 200, 500, 1000 14 200, 300, 400 15 500 16 50, 100, 200, 500, 1000 17 100, 200, 1000 18 150, 250, 500, 1000 19 50, 100, 500 20 50, 75, 100, 150, 300 21 200, 400 22 50, 100, 250, 1000 23 75, 150, 300, 600, 1200, 2400, 4800, 9600 24 200, 400, 800, 1600 25 150, 250, 500, 1000, 2500, 5000 26 257 27 175, 400 28 50, 100, 200, 400, 800 29 100, 500 30 100, 200, 500 31 100, 250, 500, 1000 32 1000 33 500, 1000 34 100, 200, 500, 1000 35 100, 200, 500, 1000 36 n:q = 1:1, 2:1, 5:1, 10:1 (n: sample size, q: number of parameter estimates 37 100, 250, 500, 1000, 2000 38 100, 300 39 500, 1000 40 100, 500 41 150, 300, 500 42 150, 250, 500, 1000, 2500, 5000 43 100, 500, 1500 44 50, 1200 45 100, 200, 400, 800, 1600, 3200 46 150, 200, 300, 500, 1000, 5000 47 250, 500

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44 Table 1-6. Number of replicatio ns in the past robustness studies Paper number Number of replications per each condition 3, 4, 23, 24 300 8, 31 500 9, 21 100, 300 10, 12, 14, 19, 20, 38, 39 100 11, 43 20 13, 16, 17, 18, 25, 28, 34, 35, 42, 47 200 15, 40 50 18 400 22 100, 400, 1000, 2500 32 25 33 1000 36 2000 37 16500 41 120 45 600

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45 CHAPTER 2 DESIGN OF MONTE CARLO SIMULATION STUDY Monte Carlo sim ulation relates to or involve s the use of random sa mpling techniques and often the use of computer simulation to obtain ap proximate solutions to mathematical or physical problems especially in terms of a range of valu es each of which has a calculated probability of being the solution (Fan et al ., 2002; Paxton et al., 2001). Monte Carlo simulation simulates the sampling process from a defined population repeat edly by using a computer instead of actually drawing multiple samples to estimate the sampling distributions of the events of the interest. Data Generation There are several methods for generating non -normal multivariate data for simulation (Mattson, 1997; Reinartz et al., 2002; Vale & Ma urelli, 1983). In some of the past robustness studies of SEM the performance of the Vale a nd Maurelli data generation method was assessed. This method has received much attention due to its flexibility (Harlo w, 1985; Mattson, 1997). The Vale and Maurelli method is an extensi on of Fleischmans power function method for univariate case (Vale & Maurelli, 1983). Fleishmans Power Transformation Method Fleishman (1978) introduced a method to simulate univariate non-nor mal conditions with desired degrees of skewness and kurtosis. This method uses polynomial transformation to transform a normally distributed va riable to a variable with sp ecified degrees of skewness and kurtosis. The polynomial transformation shown in Fleishman (1978) takes the form: 23,YabZcZdZ where Y is the transformed non-normal variable with specified population sk ewness and kurtosis, Z is a unit normal variate (i.e., a normally distri buted variable with population mean of zero and variance of one), and a, b, c and d are coefficients needed for transforming the unit normal

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46 variate to a non-normal variable with specified degrees of popul ation skewness and kurtosis. Of the four coefficients, ca Vale and Maurelli Data Generation Method Vale and Maurelli (1983) presented a procedure for generating multivariate non-normal data that is related to Fleishmans power tr ansformation method. By using the Fleishmans power transformation method, two normal variates, 1Z and 2Z can be transformed to two nonnormal variables 1X and2X, each with known skewness and kurtosis: 23 11111111, X abZcZdZ and 23 22222222. X abZcZdZ Once the degrees of skewness and kurtosis are known, the coefficients are available. In addition to these coefficients, the m odeled population correlation between the two non-normal variables 1X and2X can be specified as12R. Once 12Ris set, and the Fleishman coefficients for the specified skewness and kurtosis co nditions of the two variables (1X and2X) are available, Vale and Maurelli (1983) showed that the following relation exists: 23 1212121212 12 12(339)(2)(6), R bbbddbddccdd where is the correlation between the two normal variates, 1 Z and2 Z and it is called intermediate correlation. All elements of the equation above are known except the bivariate intermediate correlation, This bivariate intermediate correlation coefficient, must be solved for all possible pairs of variable s involved. These intermediate correlation coefficients are then assembled in proper order into an intermediate correlation matrix and multivariate normal data are generated by using

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47 y Fz where FF ~, MVNz0Iis 1. k In three previous robustness studies with Robust ML, Chou et al. (1991), Finch et al. (1997), and Hoogland (1999) used the Vale a nd Maurelli method for data generation and problems were not reported. The Vale and Ma urelli method based on Fleishman formulas was used to generate the non-normal data in the present study. The simulation study had 384 cells (the combination of four sample sizes, four distributional characteristics, th ree models, four estimation met hods, and two methods for setting the scale). Estimation Methods As stated in Chapter 1, four estimation methods were studied: Maximum Likelihood method (ML) Robust Maximum Likelihood method (Robust ML) Generalized Least Squares method (GLS) Robust Generalized Least Squares method (Robust GLS) Sample Sizes As stated in Chapter 1, 200 was chosen as the minimum sample size to avoid problems of non-convergence and improper solution. The sample sizes that were stud ied are 200, 400, 800, and 1200. The sample size, 200 is considered to be small, 400 and 800 are considered to be medium, and 1200 is judged to be large. The sample sizes 200 and 400 were often investigated in past robustness studies and are al so often in this range in obser vational research according to Table 1-1, Chou et al. (1991), and Hoogland (1 999). The sample size of 1200 was chosen to investigate whether there will be important improvements from sample size 800 to 1200.

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48 Distributional Characteristics The degrees of non-normality are reflected by various skewnesses and kurtoses of the variables. In previous studies, several classifications for categorizing the degree of non-normality for simulation were introduced (Harlow, 1985; Curran et al., 1996; Muthen & Kaplan, 1985, 1992; Potthast, 1993; Hoogland, 1999). In the pr esent study, four different levels of nonnormality were selected to reflect distributions thought to be common in the social sciences: Zero skewness and zero kurtosis (normal distribution) Zero skewness and equal negative kurtosis (plat ykurtic ): Each variables kurtosis is -1.15. Zero skewness and equal positive kurtosis (l eptokurtic): Each variab les kurtosis is 3. High equal skewness and high equal kurtosis: Ea ch variables skewness is 2 and kurtosis is 6. The baseline case of zero skewness and zero kurtosis affords opportunity for comparison and contrast with zero skewness but negative kurtosis (-1.15) re presenting almost uniform distribution (platykurtic), a peaked (leptokur tic) distribution with zero skewness but positive kurtosis (3), and a distribution with both high skewness (2) and kurtosis (6). The effects of nonnormality on standard errors have not been inve stigated in Robust GLS. Thus, these non-normal distributional conditions are st udied in the present study. Model Characteristics Two factors with three indicator s per factor, three f actors with three indi cators per factor for hybrid model analysis, four factors with three or four indicators per fa ctor, and five factors with three indicators per factor for CFA and hybrid models we re studied in previous robustness studies of Robust ML (Chou et al., 1991; Finch et al., 1997; Hoogland, 1999).Three CFA models were chosen as population models in the present study. The CFA model 3F3I consists of three factors with three indicators per factor, the CFA model 3F6I consists of three factors with six

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49 indicators per factor, and the CFA model 6F3I includes six factors with three indicators per factor. Three-factor and six-factor CFA models were chosen, because these numbers of factors cover the range typically found in CFA studies. For the models 3F3I and 6F3I, the three factor loadings for each factor were 0.6, 0.7, and 0.8. For the model 3F6I, the six factor loadings for each factor were 0.6, 0.6, 0.7, 0.7, 0.8, and 0. 8. In addition, for each population model the factor correlations were 0.3, because this is most often chosen in the previous studies and also often found in CFA studies. This study focuses on increas ing the number of fact ors and indicators for model characteristic and exclude s the effects of factor loadings and factor correlation. The average of the factor loadings in each CFA model is 0.7. Analytical Model Characteristics The data matrix typically used for computati ons in SEM programs is a covariance matrix, and also the general rule is that a covarian ce matrix should be analyzed. However, in many social/behavioral sciences applications the scales of the observed variable s are often arbitrary or irrelevant. Therefore in many cases a correlation matrix is anal yzed instead of a covariance matrix for convenience and interpretational purpose s. The analysis of a correlation matrix may cause the following problems (Cudeck, 1989; Jreskog & Srbom, 1996): (a) modification of the model being analyzed, (b) incorrec t values of test statistic, and (c) incorrect standard errors. Boomsma (1983) concluded that the analysis of correlation matrices lead to imprecise values for the parameter estimates in a struct ural equation model. She specifically found a problem with the estimation of standard erro rs for the parameter es timates. Browne (1982) pointed out that parameter estimates and chi-square statistic are unaffected when a model is scale invariant under the analysis of a correlation matrix. Suggested corr ections for the standard errors when correlations for standardized coefficien ts are used have been recommended by Browne

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50 (1982). For these reasons, a covariance matrix was analyzed and the same unit of observed variables across factors was assumed in this study. The scale of measurement for a factor can be changed without affecting the fit of the model to the data, which is called s cale indeterminacy. The data ca nnot determine the values of the parameters due to scale indeterminacy and therefore this is an identifi cation problem. Arbitrary restrictions are required to remove scale in determinacy. Two methods to solve the scale indeterminacy were investigated: factor variances are set equal to one, and factor loadings are set equal to one. In all conditions th e first observed variable has its loading set equal to one for the scale-setting method: factor loadings equal to one. Number of Replications The number of replications determines the a ccuracy with which sampling distributions of parameters estimates are approximated. The larger number of replications the more closely the sampling distributions will be approximated. The previous robustness studies on SEM have often used 100, 200, and 300 replications, according to Table 1-6. Four-hundred replications is the largest value in the past robustn ess studies of Robust ML (Finch et al., 1997). In the present study, the number of replications, 1000 was c hosen to obtain precise simulation results. Research Summary Statistics In the present study, the following research summary statistics were used: Empirical standard error The empirical standa rd error is the standard deviation of the parameter estimates. Standard errors LISREL gives estimates of th e asymptotic standard errors of parameter estimates in each sample. These can be compared with the empirical st andard errors to see if standard errors are overe stimated or underestimated. Standard error ratio More detailed informa tion about the standard errors can be obtained by studying standard error ratio, i.e., the ratio s of the standard errors to the empirical standard error (Jonsson, 1997).

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51 Standard error ratios were used instead of m ean absolute relative bias for standard error estimation because mean absolute relative bias cannot detect underestimation or overestimation of standard error estimates nor can it detect variation in bias in the standard errors across different parameters and/or parameter values. Thus standard error ratio estimates were compared for all combinations of sample size, degree of non-normality, model, estimation method, and scale-setting method. In addition, although all methods provide cons istent estimates of model parameters, at some sample sizes estimation bias may vary over estimation methods and there may be an interactive effect of estimation method with one or more of the other factors in the study. For this reason, the bias of the parameter estimation was also investigated. For the reasons cited in regard to standard errors, parameter estimates were compared instead of mean absolute relative bias for parameter estimation. Thus, parameter es timates were compared for all combinations of sample size, degree of non-normalit y, model, estimation method, a nd scale-setting method. Ways to Summarize and Present Results Because Monte Carlo simulations can produce a great deal of data, data summary and presentation of results are importa nt features in simulation studies. There are three main ways to present results: descriptive, graphical, and inferential (Pax ton et al., 2001), though clearly inferential procedures can be used to supplement descriptive and graphical presentation of results. Descriptive statistics (i.e., m ean, variance, mean relative bi as, correlation, etc.) present information concisely and simply. Graphical tech niques include using figu res such as box plots, scatter plots, or power curves. Inferential t echniques allow for the formal testing of the significance of the design factors as well as the computation of various effect size estimates. The inferential approach ha s the following advantages:

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52 The statistical significance and importance of th e main and interaction effects of the factors in the design can be determined, and the presentation can focus on effects that are both statistically significant and important effects. Designation of effects as si gnificant and important is ba sed on objective criteria. Selecting effects that are si gnificant and important for presentation can allow for concise summarization of a large amount of data. In the present study, inferential techniques such as Welch-James test and eta squared statistic were used because of the advantages above.

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53 CHAPTER 3 MONTE CARLO STUDY RESULTS A Monte Carlo sim ulation study was conducted to examine the effects of five factors on parameter estimates and standard error esti mates for CFA model. These factors were: 1. CFA model complexity, with th ree levels: a model with three factors and three indicators per factor (Model 3F3I), a model with three factors and six indicators per factor (Model 3F6I), and a model with six factors and three indicators per factor (Model 6F3I). 2. Sample size with four levels: 200, 400, 800, and 1200. 3. Distribution with four levels identified by the skewness 1 and kurtosis 2 of the distribution used for all indicators within a model: 12, (0, 0), (0, -1.15), (0, 3) and (2, 6). The (0, 0) distribution is a standard normal distribution and the multivariate distribution is also normal. The (0, -1.15) di stribution is a short-tailed distribution, the (0, 3) distribution is a long-tailed distribution, and the (2, 6) distribution is a skewed and long-tailed distribution. 4. Estimation method with two levels (GLS and ML) that could affect estimates and four levels (GLS, Robust GLS, ML, and Robust ML) that could affect standard errors. The robust procedure and a non-robust procedure (GLS and Robust GLS; ML and Robust ML) within the same estimation method produce exactly the same parameter estimates, whereas GLS, Robust GLS, ML, and Robust ML generate different standard error estimates. 5. Scale-setting method with two le vels: setting factor variances to unity and setting factor loadings to unity. The first three factors are betw een-subjects factors. For each co mbination of levels of the between-subjects factors 1000 re plications were conducted. Preliminary ANOVA Tests For Model 3F3I, for example, the population valu e for three of the fact or loadings is 0.6. Evidence that the results are similar for the thr ee indicators for which the factor loading is 0.6 would permit simplification of the analyses and re porting, as an analysis of the estimates and standard errors for only one of th e three parameters would be suffici ent. This approach can also be applied to the three indicators for which the fact or loadings are 0.7, to the three indicators for which the factor loadings are 0.8, and also to ot her parameters such as factor correlations and

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54 residual variances with the same population value. Due to the likelihood that estimates have a non-zero sampling correlation for a particular pa rameter, repeated measures ANOVA tests were conducted to investigate differences among the m eans of parameter estimates with the same population value for each of the combinations of the factors; ANOVA test s were also conducted for the means of standard error estimates. The results of the ANOVA tests for parameter estimates are presented in Table 3-1 and 32. The results are the frequency of significance differences among mean parameter estimates per 16 combinations of distribution and sample size within each combination of parameter value, model, and estimation method. In all cases the frequency was three or smaller. Even when the ANOVA test was significant, the means of the parameter estimated were very similar. For example, when the population factor loading was 0.6 in Model 3F3I, GLS was used, and factor variances were set equal to one, differences amon g the three estimates were significant in three of the 16 combinations. The smallest p value [ F (1.99, 1989.81] > 3.51, p = 0.0303] occurred for the sample size of 200 in combina tion with the (0, -1.15) distribu tion. Mean parameter estimates for this condition are presented in Table 3-3. Inspection of the resu lts in Table 3-3 indicates that the means of parameter estimates with the same population value of 0.6 are similar although the ANOVA test resulted in significant differences. The results of the ANOVA tests for standard e rror estimates are presented in Table 3-4 and 3-5. The number of significant te sts of mean equality of standard error estimates per 16 combinations of distribution and sample size in e ach combination of parameter value, model, and estimation method is two or fewer. The means of st andard error estimates for parameters with the same population value were similar even when the F test was significant. For standard error estimates of factor correlation in Model 6F3I, Robust ML, and setting factor variances to one as

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55 an example, the numbers of significance are tw o in Table 3-6. The combination of a (2, 6) distribution and a 1200 sample size resulted in the smallest p value [F (13.64, 11033.76) > 2.34, p = 0.0034]. Mean parameter estimates for this combination of distribution and sample size are reported in Table 3-6. Inspection of the results in Table 3-6 indica tes that the means of standard error estimates with the same population value of 0.3 are quite similar, differing only at the thousandths place even though the ANOVA test resulted in a significant difference. In sum, the results indicate that estimation by a particular estimation method (e.g., ML) of the same parameter type (e.g. a factor loading), with the same value results in mean estimates and standard errors that are very similar. Therefore, in the following section, results are presented for just one of the parameters of the same type and value. Welch-James Tests and Eta Squared Statistics Using results from Johansen (1980), Keselm an et al. (1993) show ed how to apply the Welch-James (WJ) test to repeated measures designs and presented evidence that this method provides better control of Type I error rates than does MANOVA for repeated measures when covariance matrices are unequal ac ross levels of the between-subjects factors. In the present study covariance matrix heterogeneity for the para meter estimates would certainly occur for the various sample sizes and therefore the WJ test was used to test hypotheses about parameter estimates. In testing hypotheses using the WJ method, a saturate d means model was used and all possible main effects and interactions were tested. Consider the linear model (Lix and Keselman, 1995): Y=X + where Y is an N p matrix of scores on p dependent variables or p repeated measurements, N is the total sample size, X is an N r design matrix consisting entirely of zeros and ones with

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56 rank(X) = r is an r p matrix of population means, and is an N p matrix of random error components. Let jY ( j = 1, r ) denote the nj p submatrix of Y containing the scores associated with the nj subjects in the jth group (cell). In the present it is typically assumed that the rows of jY are independently and normally distributed, with mean vector j and variancecovariance matrix j [i.e., N (jj,)], where jj1jp=( ), the jth row of and jj ( j j ). Specific formulas for estimating jand as well as an elaboration of jY are provided in Lix and Keselman (1995). In the present study, there were three betw een-subjects factors: sample size with four levels, distribution with four levels, and mode l with thee levels. Each combination of these factors constitutes a cell. Conse quently there were 48 cells. Each cell was replicated 1000 times. So 48000.N In each WJ analysis, estimation method was the only repeated measure. For analysis of bias of parameter estimates, ML and GLS estimates were analyzed. So 2. p For analysis of standard errors, ML, Robust ML, GLS, and Robust GLS estimates were analyzed. So 4. p In all WJ analyses all possible main effect s and interactions of between-subjects factors were included in the model. So48. r The general linear hypothesis is :=,0HR 0 where T=,ROU O is a Odfr matrix which controls cont rasts on the independent groups (between-subjects) effect(s) with rank(O) = Odfr, U is a U p df matrix which controls contrasts on the within-subj ects effect(s) with rank(U) = Udfp, is the Kronecker or direct product function, and superscript, 'T' is the tr anspose operator. For multivariate independent

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57 groups designs, U is frequently an identity matrix of dimension p (i.e., pI). The R contrast matrix has ()()OUdfdf rows and rp columns. In the equati on of the hypothesis above TT 1r=vec()=[] In other words, is the column vector with rp elements obtained by stacking the columns of T. The zero (0) column vector of the equa tion of the hypothesis is of order OUdfdf [See Lix & Keselman (1995) for illustrative examples]. The generalized test statistic given by Johansen (1980) is TT-1 WJ =()()(),TR R RR where estimates and 11rr =diag [/n/n], a block matrix with diagonal elements jj /n. This statistic, divided by a constant, o (i.e., WJT/ o ), approximately follows an F distribution with 1=OU dfdf and 211=(+2)/(3A) where 11=+2A-(6A)/(+2) o The formula for the statistic A is gi ven by (Lix and Keselman, 1995) r TT-12TT-12 jj j j=11 A=[tr{()}+{tr(())}]/(n-1) 2 RR RRQ RR RRQ, where tr is the trace operator and jQ is a symmetric block matrix of dimension rp associated with jX, such that the (s, t) th diagonal block of jp=QI if s = t = j and is zero matrix (0) otherwise. The scale-setting method is known to affect the si ze of the factor loadi ng, factor variances, and factor covariances. As an example, consider the effect of scale-setting method on the factor loadings. Recall that the factor loadings were 0.6, 0.7, or 0.8 when the scale was set by specifying factor variances equal to unity. Wh en the scale was set by specifying unit factor loadings, the loading for the first factor (i.e., th e factor that had a 0.6 loading when unit factor variances were specified) was set equal to one Thus the other two factor loadings became

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58 0.70.6 and 0.80.6, respectively. Including scale-setti ng method as a factor in the analysis would results in artificial effect of scale setting method on the estimates and perhaps artificial interactions of the other factor s with scale setting method. Ther efore the data obtained by setting factor variances equal to one and factor loadings equal to one were analyzed separately. However, the scale-setting method does not affect the size of the residual variances at all and the results obtained by setting factor variance equal to one and fact or loadings equal to one are exactly the same. Thus only the data obtained by setting factor varian ces equal to one was analyzed and presented in the present study. For the data when factor variances were set equal to one, seven WJ analyses were conducted: three were for results estimating the 0. 6, 0.7, and 0.8 factor loadings, respectively; one was for the results estimating the 0.3 factor correlation, and three were for the results estimating the 0.64, 0.51, and 0.36 residual varian ces, respectively. For the data when factor loadings were set equal to one, four WJ analyses were conducted: two were for results estimating the 0.70.6 and 0.80.6 factor loadings, respectively, one was for the results estimating the 0.36 factor variance, and one was for results estimating the 0.108 factor covariances. Residual variances are not affected by the scale-setting method, so analyses of the residual variance estimates were not conducted again. Because of the large sample size, it was e xpected that many effects would be significant even if the effects were quite small. To a ddress this problem an effect-size measure was adopted. An effect-size measure is a standard ized index, estimates a parameter that is independent of sample size, a nd quantifies the magnitude of th e difference between populations or the relationship between explanatory and resp onse variables (Olejnik & Algina, 2003). Two broad categories of effect size ar e standardized mean differences and measures of association or

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59 the proportion of variance explaine d (Olejnik & Algina, 2003). In the present study eta squared was used as an effect size. Eta squared is the proportion of the total su ms of squares that is attributed to an effect: Total EffectSS SS2, where EffectSS is the sum of squares for the effect for wh ich the effect size is being estimated and TotalSS is the total sum of squares. These sums of squares were calculated by using an ANOVA for repeated measures. The coefficient 2 was used rather than2 (Omega squared is an estimate of the dependent variable population variability accounted for by the independent variable.) Because the latter is based on the assumption of covariance homogeneity and compound symmetry but the former is not. Compound symmetry is an assumption about the variances and covariance of the repeated measures and al l correlations are equal under compound symmetry. Effects on Parameter Estimates Tables 3-7 and 3-8 contain results for estim ation when the scale was set by specifying factor variances equal to one. Table 3-7 presents the F statistics for the WJ tests and Table 3-8 presents eta squared statistics for all effects with significant F statistics in Table 3-7. Inspection of the results in Table 3-8 indicates that most ef fects were quite small, with only the effect of estimation method on estimates of the residual variance accounting for as much as approximately 5% of the total sum of squares. To select effects for further examination the following rules were applied. For each column, effects which did no t enter into higher orde r interactions and for which eta squared was at least 0.01 were selected.

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60 Applying the rule, no factors or interactions had important effects on estimation of the factor correlation and only estimation procedur e had an important effect on estimates of the factor loadings. This effect o ccurred only when the popul ation factor loading was 0.8. Table 3-9 contains marginal means of factor loading es timates, classified by estimation method, for a population factor loading equal to 0.8. Inspection of Table 3-9 s uggests the effects of estimation method on estimates of the factor loadings were trivial in size, although GLS estimates have more bias than ML estimates. Several factors had important effects on estim ates of the residual variance. Table 3-10 contains the means by estimation and sample size for estimates of populat ion residual variances. The results show that ML estimates have mi nimal bias at all sample sizes; whereas GLS estimates have more substantial bias when sample size is 200 or 400 and suggest the GLS estimates are not unbiased even when the sample size is 1200. Table 3-11 presents the F statistics for the WJ tests and Table 3-12 presents eta squared statistics for all effects with significant F statistics in Table 3-11. Both tables contain results for estimation when the scale was set by specifying f actor loadings equal to one. Inspection of the results in Table 3-12 indicates that most effects on parameter estimates of factor loadings and factor correlations were quite small and none of them did accounted for even 1% of the total sum of squares. Because none of the effect accounted for even 1% of the total sum of squares, Table 3-13 presents the grand mean estimates of parameter by population parameter value. The results indicate that the grand means are ve ry similar to their population values. Effects on Standard Error Ratio Estimates LISREL provides estimates of the asymptotic st andard errors of parameter estimates in each sample. These asymptotic standard errors are called standard errors in the present study. An

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61 empirical standard error is the standard deviatio n of the distribution of the parameter estimates. For each population parameter value, empirical standard errors were calculated for each combination of the five factors. An empirical standard error provides an estimate of the actual standard error for a particular co mbination of population parameter value and the five factors, an estimate that does not rely on large sample theory for its validity. The ratio of the standard errors to the empirical standard error can be used to determine if the standard error overestimates or underestimates the sampling variab ility of the estimates. The WJ tests for the standard error ratios were conducted and eta squa red statistics for the standard error ratios were calculated in the present study. Tables 3-14 and 3-15 contain re sults for standard error ratio s when the scale was set by specifying factor variances equal to one. Table 3-14 presents the F statistics for the WJ tests and Table 3-15 presents eta squared statis tics for all effects with significant F statistics in Table 3-14. For standard error ratio estimates, every effect wa s significant in the WJ te sts as shown in Table 3-14. To select effects for further examination the same rules used for the effects on parameter estimates were applied. Table 3-15 indicates that when the populati on factor loading was 0.6, sample size accounted for more than 1% of the sums of s quares. Table 3-16 contains marginal means of standard error ratios for factor loading estimates, classified by sample size, for a population factor loading equal to 0.6. As the sample size in creases the bias of standard error estimates of the factor loadings decreases. Although not included, tables of marginal means when the factor loading was 0.7 or 0.8 show a similar pattern. Inspection of Table 3-16 suggests the effects of sample size on standard error estimates of the fact or loadings is consistent with theory that

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62 standard error estimates will converge to their tr ue values as the sample size increases without bound. As shown in Tables 3-14 and 3-15, for each factor loading population value, the interaction of estimation method and distribution is significant and accounts for a substantial portion of the total sums of squares for the standard error ratio. Table 3-17 contains mean standard error ratios of factor loading estimates by estimation method a nd distribution. The results show that standard errors of ML estimates have minimal bias only when the distribution is normal, whereas Robust ML standard error estimates have a relatively small bias regardless of the distribution. GLS standard error estimates have about the same bias as ML estimates, with slightly more bias for the normal distribution and slightly less for the short-tailed distribution (0, -1.15). Robust GLS estimates of standard errors tend to have sli ghtly more bias than do robust ML estimates. For each estimation method, the standa rd error ratio varies as a fu nction of the distribution. The mean standard error ratio tends to be the highe st for the (0, -1.15) di stribution and indicates overestimation when ML or GLS was used and s light underestimation when the robust variants were used. With the (2, 6) distri bution, the mean standard error ra tios tend to be the smallest. In particular GLS and ML standard e rrors are gross underestimates with this distribution. When the distribution is long tailed (0, 3) estimates also tend to be too small and the ML and GLS estimates strongly underestimate the standard errors. The results of the WJ tests and eta square d statistics indicated a significant three-way interaction among sample size, model, and the di stribution on the standard error ratio for the factor correlation, accounting for 5% of the to tal sum of squares. The means for the standard error ratios of factor correla tion as a function of sample size, model, and distribution are presented in Table 3-18. The standa rd error ratio varies as a function of the sample size within

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63 each model and distribution. The bias of the standard error estimates tends to decrease as the sample size increases for the (2, 6) distribution and the standard error estimates in the (2, 6) distribution tend to be underestim ated regardless of the model. In spection of Table 3-18 indicates that for the other distributions th e bias tends to decrea se as the sample size increases from 200 to 400, but the effect of further increases of sample size on bias is irregula r, particularly in the normal distribution and (0, -1.15) distribution. Shown in Appe ndix A are empirical standard errors and mean standard errors and mean sta ndard error ratios, by m odel, sample size, and distributions. Results are presented in four tabl es, one for each estimation method. The results show that the empirical standard errors and mean standard errors decreased as the sample size increased from 200 to 1200. However as the ratio of the mean standard error to empirical standard error approached 1.0 as a function of sa mple size, relatively small differences between the mean standard error and empirical standard e rror resulted in ratios th at varied around 1.0. For example, with ML estimation, Model 3F3I and a normal distribution, the mean standard error and empirical standard error were 0.0607274 and 0.0598677 400,N0.0430910 and 0.0448250 800,N and 0.0351752 and 0.0341341 1200,N resulting in standard error ratios of 1.014, 0.961, and 1.031, respectively. T hus, although at first glance the behavior of the standard error ratio does not seem to be consistent with the expectation that the bias of standard errors would decline as the sample size increased, it seems more likely that the results mean that for all distributions except the (2, 6) distribution, the standa rd error became nearly unbiased when the sample size was 400 and the variation of the standard error ratio around 1.0, is due to sampling error. The interaction of distribution and estimati on method accounted for approximately 8% of the total sums of squares in the standard error ratio for the factor correlation. The means for the

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64 standard error ratios of factor correlations as a function of di stribution and estimation method are presented in Table 3-19. When th e distribution was normal, (0, -1. 15) or (0, 3), ML and Robust ML showed better performances than GLS and Robust GLS in the bias of standard error estimates, whereas the robust estimation procedur es showed better performances than the nonrobust estimation procedures for the non-normal dist ributions, with a strong advantage for the (2, 6) distribution. Tables 3-14 and 3-15 indicate a significant main effect of sample size on the standard error ratio that accounts for 1% of the total sums of squares when the residual variance was 0.36. Table 3-20 contains the marginal means for this main effect. Inspection of Table 3-20 suggests the effects of sample size on standard error esti mates of the residual variance are consistent with theory that estimates will conve rge to their true va lues as the sample size increases without bound. That is, the bias of standard error estimat es decreases as the sample size increases. Similar results, not tabled in the dissertation, were found when the population residual variance was 0.51 or 0.64. When the residual variance was 0.36, there was a significant Model Distribution interaction that accounted for 1% of the total sums of squares. The mean standard error ratios are presented in Table 3-21. Inspection of the resu lt in Table 3-21 indicates that the bias of the standard error estimates increase as the degr ee of non-normality increases regardless of model type, but the effect of non-nor mality appears to be stronger when there are more indicators. Similar results, not tabled in the dissertation, were found when the population residual variance was 0.51 or 0.64. The results of WJ tests indi cated a significant two-way inte raction between the estimation method and distribution accounting for 20% of th e total sum of squares when the residual

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65 variance was 0.64, 14% when the residual variance was 0.51, and 10% when the residual variance was 0.36. The means for the standard erro r ratios of residual vari ances as a function of estimation method and distribution are presented in Table 3-22. Fo r the normal distribution, ML and Robust ML showed better performances than GLS and Robust GLS in the bias of standard error estimates, whereas the robust estimation pr ocedures showed better performances than the non-robust estimation procedures for the non-nor mal distributions. Specially, ML has the minimal bias of standard error estimates for th e normal distribution, whereas Robust ML has the smallest bias of standard error estimates for the non-normal distributions For the distributions with positive kurtosis (0, 3), and high skewness and high kurtosis (2, 6), the bias of standard error estimates tends to decrea se as the population parameter va lues (0.64, 0.51, and 0.36) decrease. Tables 3-23 and 3-24 contain re sults for standard error ratio s when the scale was set by specifying factor loadings equal to one. Table 3-23 presents the F statistics for the WJ tests, and Table 3-24 presents eta squared statis tics for all effects with significant F statistics in Table 3-23. Inspection of the results in Table 3-24 indica tes that most effects on standard error ratio estimates of factor loadings and factor correlations were quite sm all compared with the effects of distribution and the two-way inte raction between estimation and di stribution. To select effects for further examination the rules used when factor variances were set equal to one were applied. For the residual variance, there is no differen ce between both scale-setting methods because the scale-setting methods do not affect the estimates of residual variances. Tables 3-23 and 3-24 indicate that the Estimation Distribution interaction was significant for both factor loading population valu es and accounted for 11 % and 9 % of the total sum of squares when the factor loading was 0.70.6 and 0.80.6, respectively. The means for

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66 the standard error ratios of factor loadings as a function of es timation and distribut ion, classified by parameter value, are presented in Table 325. Differences between the robust estimation procedures and the non-robust estimation procedures are very small for the normal distribution. For the non-normal distribution, the robust estim ation procedures show ed better performance rather than the non-robust procedures. For the (0 -1.15) distribution (n egative kurtosis), the standard error estimates tend to be overesti mated by the non-robust estimation procedures and underestimated by the robust estimation procedures For the other distri butions, the standard error estimates tend to be underestimated regardless of the estimation procedure. Tables 3-23 and 3-24 indicate that the sample size effect was significant for the factor covariance and accounted about 1.5 % of the total sum of squares. Table 3-26 contains marginal means of standard error ratio estimates for fact or covariance parameter, classified by sample size, for the factor c ovariance. Inspection of Table 3-26 suggests the effects of sample size on standard error estimates of the factor covariance c onsistent with theory that bias will decrease the sample size increases and 1200 sample size had the minimal bias of standard error estimates. Although means by sample size are not reported for the other pa rameters since eta squared statistics are less than 1% of the total sum of squares, results show the same pattern of results as in Table 3-26: less bias with increasing sample size accounting for rounding 1% of the total sum of squares. Tables 3-23 and 3-24 indicate that the Estimation Distribution interaction was significant for both elements of the factor covariance matrix a nd accounted 14% of the total sum of squares for the factor variance and 5% of the total sum of squares for the factor covariance. The means for the standard error ratios of factor correlations as a function of estimation method and distribution are presented in Table 3-27. For the normal distribution, ML and Robust ML

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67 showed better performances than GLS and Robust GLS in the bias of standard error estimates, whereas the robust estimation procedures showed better perf ormances than the non-robust estimation procedures for the non-normal distribu tions. Robust ML has th e smallest bias of standard error estimates for the non-normal distributions. There are the consistent patterns between factor variances and f actor covariances with different parameter values (0.108 and 0.36), that is, the means of standard error ra tio estimates in factor variances and factor covariances showed similar behaviors. Practical Problems in Estimation As noted in Chapter 1, estimates in SEM are found by minimizing a discrepancy function. Because a direct algebraic solution to finding th e minimizer is not available for all models, estimation in SEM is generally carried out by iteration. As a result, non-convergence can be encountered when SEM is used. The iterative pr ocess is said to have converged when the criterion for convergence is met. In ML estimation in LISREL, the crite rion for convergence is a change in the likelihood from one iteration to the next that is smaller than 0.000001 (Jreskog and Srbom, 1996). The maximum number of iterati ons is set a priori and non-convergence in LISREL occurs when the change in the likelihood ne xt to last and last iteration is larger than 0.000001. Another possible problem is a non-positiv e definite matrix of second-order partial derivatives of the discrepancy f unction, a matrix called the Hessian matrix. The Hessian matrix is used to diagnose identification pr oblems and to compute standard errors. If the Hessian matrix is not positive definite the solution is classified as non-identified and, in addition, standard errors cannot be computed. In addition, unless the iterative procedure is constructed to keep variance and covariance estimates within the bounds of proper estimates, estimates outside these bound can occur, for example, negative residual variances. Such estimates are called improper estimates

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68 in the present study. For the deta ils on the problems, potential so lutions, and limita tions to these potential solutions, see Chen et al (2001), Bentler and C hou (1987), and Wothke (1993). Previous studies reported non-convergence ma inly for the ML estimation method. Nonconvergence of ML estimation method decreased wh en the sample size, the number of indicators per factor, or the size of the factor loadings increased (Ande rson & Gerbing, 1984; Boomsma, 1985). Also Hoogland (1999) reported that none of the estimation methods had substantial convergence problems for N 200, and for N = 200, the percentage of improper solutions was considerable. This percentage increased when the size of the factor loadings or the number of indicators per factor decreased. While conducting the simulation the following pr oblems occurred in some replications of some conditions: (a) non-convergence in 10,000 iterations, (b) improper estimates (e.g., a negative residual variance), and (c) non-positive definite Hessian matrix (so standard errors could not be computed). The frequency of non-convergence, improper estimates, and nonpositive definite Hessian matrix for certain combinations of model, distribution, sample size, scale-setting method, and estimation is presented in Table 3-28. Non-convergence did not occur for the normal distribution at all and mainly o ccurred for sample sizes such of 200 and 400 for the non-normal distributions. For the 3F3I model, non-convergence occurred only for ML and Robust ML, but occurred at a very low freque ncy and never when the sample size was 800 or 1200. For the 3F6I model, non-convergence, im proper estimates, and non-positive definite Hessians occurred for all estimation procedures, but only when the scale was set by specifying factor loadings equal to one, and again at a ve ry low frequency and never when the sample size was 800 or 1200. Increasing the number of indicat ors per factor caused more non-convergence, improper estimates, and non-positive definite He ssian matrices. However, setting the scale by

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69 specifying factor variances equal to one reduced the problems of non-convergence, improper estimates, and non-positive definite Hessians. For the model 6F3I, non-convergence, improper estimates, and non-positive definite Hessians oc curred only for GLS and Robust GLS and at a very low frequency. There was no non-convergen ce problem at all when the scale was set by specifying factor variances equal to one. As ha s been reported in previous studies (e.g., Boomsma & Hoogland, 2001) degree of non-normality the number of indicators per factor, and the sample size played important roles in wh ether non-convergence, improper estimates, and non-positive definite Hessians occurred. In addition, the present study su ggests that the scalesetting method has an important effect under non-normality.

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70 Table 3-1. Freque ncy of significance F tests on parameter estimates per 16 combinations of distribution and sample size: factor variances equal to one. Estimation method Model Parameter value ML GLS (0.6) 2 3 (0.7) 1 1 (0.8) 1 0 (0.3) 1 1 (0.64) 0 0 (0.51) 1 1 3F3I (0.36) 0 0 (0.6) 1 2 (0.7) 0 0 3F6I (0.8) 0 0 (0.3) 1 2 (0.64) 1 1 (0.51) 1 1 (0.36) 0 0 6F3I (0.6) 0 1 (0.7) 1 0 (0.8) 0 0 (0.3) 1 1 (0.64) 1 1 (0.51) 1 0 (0.36) 1 1 Note. indicates factor loading, : factor correlation, and : residual variance.

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71 Table 3-2. Frequency of significant F tests on parameter estimates per 16 combinations of distribution and sample size: f actor loadings equal to one. Estimation method Model Parameter Values ML GLS (0.7/0.6) 0 0 (0.8/0.6) 0 1 (i,i) = (0.36) 1 0 3F3I (i,j) = (0.108) 1 1 (0.7/0.6) 2 2 (0.8/0.6) 2 2 (i,i) = (0.36) 1 2 3F6I (i,j) = (0.108) 1 2 (0.7/0.6) 2 2 (0.8/0.6) 0 0 (i,i) = (0.36) 0 1 6F3I (i,j) = (0.108) 1 1

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72 Table 3-3. Mean parameter estimate for = 0.6: Model 3F3I, GLS, N = 200, factor variances equal to one, and (0, -1.15). Three factor loadings: (0.6) Means of parameter estimates 1 0.593 2 0.593 3 0.586 Table 3-4. Frequency of significant F tests on standard error estimates per 16 combinations of distribution and sample size: f actor variances equal to one. Estimation method Model Parameter value ML RML GLS RGLS (0.6) 0 0 0 0 (0.7) 1 0 0 0 (0.8) 1 0 1 0 (0.3) 0 0 0 0 (0.64) 0 0 0 1 (0.51) 1 1 0 1 3F3I (0.36) 2 1 1 1 (0.6) 1 1 1 1 (0.7) 0 0 1 0 (0.8) 1 0 1 0 (0.3) 1 2 1 1 (0.64) 1 2 1 2 (0.51) 1 1 1 1 3F6I (0.36) 0 1 0 0 (0.6) 0 0 1 0 (0.7) 0 0 0 0 (0.8) 2 1 2 1 (0.3) 2 2 1 2 (0.64) 1 2 0 1 (0.51) 0 2 0 2 6F3I (0.36) 2 1 2 1

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73 Table 3-5. Frequency of significant F tests on standard error estimates per 16 combinations of distribution and sample size: f actor loadings equal to one. Estimation method Model Parameter value ML RML GLS RGLS (0.7/0.6) 1 1 1 2 (0.8/0.6) 1 1 1 2 (i,i) (0.36) 1 2 1 2 3F3I (i,j) (0.108) 0 1 1 2 (0.7/0.6) 1 1 2 2 (0.8/0.6) 2 1 1 2 (i,i) (0.36) 2 2 2 2 3F6I (i,j) (0.108) 1 0 1 2 (0.7/0.6) 1 1 1 1 (0.8/0.6) 1 0 0 0 (i,i) (0.36) 0 0 0 0 6F3I (i,j) (0.108) 1 0 0 0 Table 3-6. Mean standard error estimates for = 0.3: model 6F3I, Robust ML, N = 1200, factor variances equal to one, and (2, 6). Fifteen factor correlations: (0.3) Means of standard error estimates SE19 0.0400 SE20 0.0399 SE21 0.0398 SE22 0.0396 SE23 0.0396 SE24 0.0399 SE25 0.0397 SE26 0.0397 SE27 0.0395 SE28 0.0396 SE29 0.0395 SE30 0.0397 SE31 0.0397 SE32 0.0397 SE33 0.0396

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74 Table 3-7. F statistics (degrees of freedom) for WJ te sts for effects on parameter estimates: scale set by specifying factor variances equal to one. Parameter (0.6) (0.7) (0.8) (0.3) (0.64) (0.51) (0.36) Between-subjects S 46.02 (3, 22506.63) 61.53 (3, 21599.19) 72.73 (3, 21198.98) 1.83 (3, 24825.76) 620.72 (3, 20239.98) 446.12 (3, 21093.75) 312.53 (3, 20896.37) D 1.66 (3, 17775.07) 5.64 (3, 17700.60) 3.12 (3, 17406.09) 0.62 (3, 17283.86) 14.52 (3, 18013.56) 9.82 (3, 17424.44) 15.52 (3, 15742.61) M 23.31 (2, 19266.16) 38.43 (2, 18800.59) 45.71 (2,18258.67) 7.72 (2, 20687.19) 219.81 (2, 17766.82) 194.44 (2, 18747.50) 67.91 (2, 18928.08) S*D 1.0296 (9, 18427.51) 0.82 (9, 18228.70) 0.72 (9, 18073.82) 1.05 (9, 18432.73) 2.32 (9, 18206.46) 1.53 (9, 17812.25) 3.52 (9, 16505.48) S*M 6.12 (6, 18082.12) 7.33 (6, 17408.11) 8.10 (6, 17138.26) 1.78 (6, 20066.18) 26.30 (6, 16326.06) 23.27 (6, 17217.24) 8.05 (6, 17829.92) D*M 0.84 (6, 14311.52) 0.46 (6, 14305.69) 0.57 (6, 14157.82) 0.75 (6, 13965.46) 2.21 (6, 14588.77) 2.70 (6, 14366.58) 0.13 (6, 13566.43) S*D*M 1.16 (18, 16850.26) 0.69 (18, 16726.97) 0.47 (18, 16682.06) 0.99 (18, 16940.70) 0.92 (18, 16705.38) 0.95 (18, 16628.99) 0.96 (18, 16075.00) Within-subjects E 12178.18 (1, 16540.83) 17764.71 (1, 16636.72) 25686.78 (1, 16649.31) 4520.48 (1, 15325.14) 152399.97 (1, 17241.46) 108604.31 (1, 16748.83) 52581.77 (1, 15320.01) E*S 1524.95 (3, 20451.17) 2281.22 (3, 20465.43) 3231.08 (3, 20501.19) 471.46 (3, 18942.21) 18111.71 (3, 20302.35) 13166.86 (3, 20372.53) 6457.34 (3, 19094.80) E*D 30.04 (3, 9590.41) 45.51 (3, 9692.51) 85.56 (3, 9709.39) 2.09 (3, 8897.09) 297.00 (3, 10375.78) 274.18 (3, 9880.68) 141.91 (3, 9052.65) E*M 1298.57 (2, 11765.34) 1884.21 (2, 11864.30) 2434.13 (2, 11985.07) 242.89 (2, 11306.97) 12160.34 (2, 12197.84) 8728.98 (2, 11987.15) 4810.11 (2, 11892.42) E*S*D 4.93 (9, 15655.19) 5.85 (9, 15702.67) 12.01 (9, 15683.89) 0.62 (9, 14463.13) 29.90 (9, 15870.74) 29.11 (9, 15730.18) 15.06 (9, 14699.42) E*S*M 162.49 (6, 17485.34) 238.26 (6, 17568.41) 308.24 (6, 17691.47) 25.60 (6, 17204.20) 1410.96 (6, 17274.57) 1040.98 (6, 17475.85) 572.56 (6, 17603.68) E*D*M 1.57 (6, 8198.83) 3.84 (6, 8276.55) 6.43 (6, 8424.32) 0.71 (6, 7874.98) 37.14 (6, 8788.30) 35.89 (6, 8603.60) 19.99 (6, 8483.09) E*S*D*M 0.78 (18, 15209.78) 0.79 (18, 15292.40) 1.22 (18, 15427.25) 0.69 (18, 14955.98) 6.36 (18, 15340.25) 5.13 (18, 15485.72) 2.36 (18, 15525.14) Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, : factor correlation, and : residual variance.

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75 Table 3-8. Eta squared for si gnificant effects on parameter estimates: scale set by specifying factor variances equal to one. Parameter (0.6) (0.7) (0.8) (0.3) (0.64) (0.51) (0.36) Between-subjects S 0.0033 0.0044 0.0055 0.0397 0.0300 0.0221 D 0.0005 0.0003 0.0010 0.0007 0.0011 M 0.0010 0.0015 0.0018 0.0003 0.0079 0.0074 0.0030 S*D 0.0003 0.0010 S*M 0.0009 0.0010 0.0010 0.0039 0.0034 0.0012 D*M 0.0003 0.0004 S*D*M Within-subjects E 0.0053 0.0073 0.0104 0.0019 0.0693 0.0540 0.0286 E*S 0.0024 0.0033 0.0046 0.0005 0.0293 0.0233 0.0123 E*D 0.0001 0.0001 0.0001 0.0005 0.0005 0.0003 E*M 0.0009 0.0012 0.0015 0.0002 0.0085 0.0067 0.0039 E*S*D 0.0000 0.0000 0.0001 0.0002 0.0002 0.0001 E*S*M 0.0004 0.0006 0.0007 0.0001 0.0034 0.0027 0.0016 E*D*M 0.0000 0.0000 0.0001 0.0001 0.0001 E*S*D*M Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, : factor correlation, and : residual variance. Table 3-9. Marginal means by estimation for estimates of = 0.8 Estimation Marginal means GLS 0.788 ML 0.800 Table 3-10. Mean estimates of residual variance parameters by parameter value, estimation method, and sample size. Sample size Parameter value Estimation 200 400 800 1200 0.36 GLS 0.310 0.334 0.347 0.351 ML 0.353 0.356 0.359 0.359 0.51 GLS 0.442 0.475 0.492 0.498 ML 0.506 0.508 0.509 0.509 0.64 GLS 0.554 0.596 0.617 0.625 ML 0.634 0.637 0.638 0.639

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76 Table 3-11. F statistics (degrees of freedom) for WJ tests for effects on parameter estimates: scale set by specifying factor loadings equal to one. Parameter (0.7/0.6) (0.8/0.6) (i,i) = (0.36) (i,j) = (0.108) Between-subjects S 30.28 (3, 22125.35) 38.49 (3, 22189.49) 8.57 (3, 22208.53) 19.33 (3, 24267.26) D 4.77 (3, 16883.50) 7.66 (3, 16473.47) 2.40 (3, 17582.66) 0.21 (3, 17633.72) M 2.41 (2, 18254.97) 4.19 (2, 18080.90) 26.54 (2, 18840.57) 7.75 (2, 20592.78) S*D 1.93 (9, 18240.52) 1.41 (9, 17942.50) 1.14 (9, 18356.55) 0.82 (9, 18449.22) S*M 1.69 (6, 17835.21) 0.79 (6, 18077.52) 6.37 (6, 17881.18) 0.95 (6, 19593.93) D*M 1.08 (6, 13645.46) 0.51 (6, 13494.24) 0.86 (6, 14207.07) 0.42 (6, 14255.28) S*D*M 0.63 (18, 16747.68) 1.09 (18, 16671.06) 1.19 (18, 16820.97) 1.17 (18, 16942.48) Within-subjects E 2.62 (1, 15632.45) 3.20 (1, 14317.90) 11807.82 (1, 16404.87) 1207.51 (1, 15341.86) E*S 1.61 (3, 20172.44) 1.90 (3, 19755.23) 1444.36 (3, 20283.60) 214.33 (3, 19103.04) E*D 0.21 (3, 9092.03) 0.90 (3, 8681.75) 31.49 (3, 9653.42) 9.39 (3, 9082.70) E*M 1.51 (2, 11208.28) 0.44 (2, 10793.27) 1243.90 (2, 11644.45) 425.01 (2, 11327.47) E*S*D 0.89 (9, 15496.59) 0.99 (9, 15387.27) 5.29 (9, 15680.82) 2.45 (9, 14733.99) E*S*M 0.91 (6, 17372.35) 1.10 (6, 17429.38) 151.13 (6, 17338.56) 68.39 (6, 17007.79) E*D*M 0.54 (6, 7845.83) 1.42 (6, 7776.98) 1.55 (6, 8244.30) 3.37 (6, 8050.43) E*S*D*M 0.87 (18, 15166.03) 0.78 (18, 15366.89) 0.76 (18, 15233.44) 1.00 (18, 14969.96) Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, and : factor correlation.

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77 Table 3-12. Eta squared for si gnificant effects on parameter estimates: scale set by specifying factor loadings equal to one. Parameter (0.7/0.6) (0.8/0.6) (i,i) = (0.36) (i,j) = (0.108) Between-subjects S 0.0024 0.0030 0.0005 0.0018 D 0.0003 0.0005 M 0.0002 0.0011 0.0003 S*D 0.0004 S*M 0.0009 D*M S*D*M Within-subjects E 0.0050 0.0005 E*S 0.0022 0.0004 E*D 0.0001 0.0000 E*M 0.0008 0.0002 E*S*D 0.0000 0.0000 E*S*M 0.0003 0.0002 E*D*M 0.0000 E*S*D*M Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, and : factor correlation. Table 3-13. Grand mean estimates of parameter by parameter value Parameter Grand mean (0.7/0.6 = 1.167) 1.176 (0.8/0.6 = 1.333) 1.345 (i,i) = (0.36) 0.358 (i,j) = (0.108) 0.107

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78 Table 3-14. F statistics (degrees of freedom) for WJ tests for effects on standard error ratio estimates: scale set by specifying f actor variances equal to one. Parameter (0.6) (0.7) (0.8) (0.3) (0.64) (0.51) (0.36) Between-subjects S 595.90 (3, 15782.04) 467.62 (3, 16318.87) 379.78 (3, 17343.85) 3603.70 (3, 23369.23) 198.16 (3, 19385.97) 242.22 (3, 20429.42) 363.96 (3, 22444.21) D 20946.67 (3, 19027.12) 26581.99 (3, 18729.86) 22200.70 (3, 18881.47) 4595.39 (3, 19551.74) 10741.40 (3, 19649.16) 7329.45 (3, 17648.10) 3889.99 (3, 18945.82) M 225.66 (2, 15794.47) 42.24 (2, 16166.66) 116.43 (2, 17599.89) 273.00 (2, 22385.22) 15.44 (2, 18824.20) 169.66 (2, 19600.56) 218.83 (2, 21405.65) S*D 48.99 (9, 17638.50) 218.89 (9, 17553.80) 101.65 (9, 17538.81) 194.24 (9, 18642.07) 49.07 (9, 17772.69) 48.22 (9, 17301.84) 96.55 (9, 17901.15) S*M 87.91 (6, 12666.42) 198.36 (6, 13141.15) 57.97 (6, 13948.56) 68.55 (6, 13948.56) 34.21 (6, 15528.44) 31.56 (6, 16452.99) 30.47 (6, 18161.94) D*M 135.74 (6, 15268.67) 127.56 (6, 15117.87) 276.67 (6, 15383.29) 263.29 (6, 15693.62) 80.08 (6, 15761.30) 111.48 (6, 14712.34) 234.54 (6, 15264.45) S*D*M 82.00 (18, 16102.75) 107.39 (18, 16146.65) 137.13 (18, 16225.82) 627.60 (18, 17017.36) 77.87 (18, 16322.40) 42.42 (18, 16228.62) 29.03 (18, 16583.77) Within-subjects E 77257.59 (3, 15303.02) 69198.35 (3, 15692.24) 36958.85 (3, 14287.87) 23486.29 (3, 14896.53) 22848.71 (3, 18411.56) 14151.02 (3, 17556.47) 11625.93 (3, 15108.66) E*S 10195.94 (9, 19079.13) 7328.54 (9, 19516.57) 2784.84 (9, 19235.63) 2703.90 (9, 18792.67) 932.12 (9, 22540.47) 497.97 (9, 22243.10) 620.77 (9, 21133.74) E*D 21183.88 (9, 14286.31) 27486.10 (9, 14395.97) 27151.98 (9, 13363.58) 1147.50 (9, 13708.24) 15919.85 (9, 14747.70) 8033.84 (9, 13891.66) 5491.45 (9, 13233.02) E*M 4459.91 (6, 12911.48) 5099.82 (6, 13239.75) 3952.74 (6, 12553.37) 1887.56 (6, 12425.43) 1215.18 (6, 15405.97) 706.68 (6, 15007.13) 473.90 (6, 13573.18) E*S*D 1497.61 (27, 21971.93) 1621.64 (27, 22161.95) 529.65 (27, 21948.32) 99.75 (27, 21536.83) 980.36 (27, 21907.35) 509.83 (27, 21498.40) 236.39 (27, 21159.85) E*S*M 1247.12 (18, 18044.97) 927.37 (18, 18551.41) 540.04 (18, 18499.14) 404.69 (18, 17733.29) 459.54 (18, 21227.91) 222.73 (18, 21268.16) 272.15 (18, 20822.39) E*D*M 1360.22 (18, 13539.20) 1946.81 (18, 13654.05) 2745.12 (18, 13203.38) 53.13 (18, 13049.66) 1163.97 (18, 14049.75) 389.33 (18, 13741.50) 247.23 (18, 13283.14) E*S*D*M 499.25 (54, 21984.87) 1017.96 (54, 22257.57) 583.71 (54, 22170.23) 157.36 (54, 21639.51) 268.91 (54, 22006.12) 150.17 (54, 22095.98) 124.57 (54, 21887.70) Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, : factor correlation, and : residual variance.

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79 Table 3-15. Eta squared for signi ficant effects on standard erro r ratio estimates: scale set by specifying factor variances equal to one. Parameter (0.6) (0.7) (0.8) (0.3) (0.64) (0.51) (0.36) Between-subjects S 0.0107 0.0065 0.0067 0.0965 0.0041 0.0070 0.0152 D 0.2918 0.3201 0.2882 0.1195 0.2300 0.1783 0.1165 M 0.0023 0.0004 0.0010 0.0039 0.0002 0.0026 0.0045 S*D 0.0008 0.0046 0.0022 0.0145 0.0019 0.0021 0.0053 S*M 0.0017 0.0051 0.0015 0.0023 0.0014 0.0010 0.0018 D*M 0.0051 0.0027 0.0057 0.0099 0.0030 0.0049 0.0101 S*D*M 0.0037 0.0043 0.0057 0.0526 0.0038 0.0038 0.0054 Within-subjects E 0.0612 0.0572 0.0602 0.0333 0.1077 0.1120 0.1008 E*S 0.0028 0.0024 0.0020 0.0070 0.0017 0.0018 0.0020 E*D 0.2290 0.2542 0.3009 0.0817 0.1960 0.1371 0.1008 E*M 0.0004 0.0005 0.0004 0.0018 0.0002 0.0010 0.0043 E*S*D 0.0013 0.0011 0.0013 0.0014 0.0012 0.0012 0.0012 E*S*M 0.0003 0.0002 0.0002 0.0007 0.0003 0.0003 0.0002 E*D*M 0.0009 0.0024 0.0064 0.0002 0.0008 0.0017 0.0029 E*S*D*M 0.0002 0.0005 0.0004 0.0007 0.0002 0.0003 0.0004 Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, : factor correlation, and : residual variance. Table 3-16. Marginal means by sample size for standard error ratio estimates of = 0.6 Sample size Marginal means of standard error ratio 200 0.907 400 0.936 800 0.937 1200 0.949

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80 Table 3-17. Mean estimates of standard error ratio for factor loading parameters by parameter value, estimation method, and distribution Distribution Parameter value Estimation (0, 0) (0, -1.15) (0, 3) (2, 6) 0.6 GLS 0.971 1.072 0.828 0.681 ML 0.994 1.102 0.838 0.683 Robust GLS 0.965 0.968 0.952 0.950 Robust ML 0.988 0.992 0.967 0.965 0.7 GLS 0.967 1.102 0.800 0.650 ML 0.989 1.138 0.808 0.652 Robust GLS 0.961 0.963 0.955 0.943 Robust ML 0.982 0.992 0.967 0.958 0.8 GLS 0.972 1.109 0.787 0.634 ML 0.991 1.142 0.795 0.633 Robust GLS 0.967 0.941 0.970 0.953 Robust ML 0.985 0.965 0.983 0.965 Table 3-18. Mean estimates of standard er ror ratio for factor correlation parameters, (0.3) by model, sample size, and distribution. Distribution Model Sample size (0, 0) (0, -1.15) (0, 3) (2, 6) 3F3I 200 0.916 0.948 0.933 0.856 400 1.010 0.992 0.988 0.883 800 0.955 1.009 0.973 0.911 1200 1.027 0.974 0.958 0.970 3F6I 200 0.941 0.932 0.946 0.898 400 1.010 1.012 0.976 0.917 800 0.942 1.007 1.003 0.923 1200 1.004 1.041 1.021 0.920 6F3I 200 0.939 0.928 0.982 0.858 400 0.990 0.986 0.954 0.933 800 1.038 0.967 0.957 0.951 1200 0.989 1.009 0.989 0.975 Table 3-19. Means estimates of standard er ror ratio for factor correlation parameter, (0.3) by estimation and distribution. Distribution Estimation (0, 0) (0, -1.15) (0, 3) (2, 6) GLS 0.974 0.974 0.968 0.856 ML 0.991 0.992 0.985 0.875 Robust GLS 0.969 0.975 0.962 0.956 Robust ML 0.986 0.993 0.979 0.977

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81 Table 3-20. Marginal means by sample size for standard error ratio estimates of = 0.36 Sample size Marginal mean s of standard error ratio 200 0.864 400 0.905 800 0.913 1200 0.920 Table 3-21. Means estimates of standard error ratio for residual variance parameter, (0.36) by model and distribution. Distribution Model (0, 0) (0, -1.15) (0, 3) (2, 6) 3F3I 0.959 0.947 0.934 0.811 3F6I 0.991 0.909 0.867 0.770 6F3I 0.956 0.922 0.914 0.827 Table 3-22. Means estimates of standard er ror ratio for residual variance parameters by parameter value, estimation method, and distribution. Distribution Parameter value Estimation (0, 0) (0, -1.15) (0, 3) (2, 6) 0.36 GLS 0.962 0.903 0.839 0.664 ML 0.982 0.910 0.840 0.653 Robust GLS 0.956 0.940 0.966 0.946 Robust ML 0.975 0.949 0.975 0.949 0.51 GLS 0.973 0.951 0.766 0.601 ML 0.992 0.972 0.760 0.592 Robust GLS 0.967 0.950 0.955 0.938 Robust ML 0.985 0.970 0.959 0.948 0.64 GLS 0.964 1.027 0.719 0.567 ML 0.986 1.057 0.715 0.556 Robust GLS 0.957 0.960 0.954 0.948 Robust ML 0.978 0.982 0.962 0.953

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82 Table 3-23. F statistics (degrees of freedom) for WJ tests for effects on standard error ratio estimates: scale set by specifying factor loadings equal to one. Parameter (0.7/0.6) (0.8/0.6) (i,i) = (0.36) (i,j) = (0.108) Between-subjects S 220.62 (3, 25086.80) 178.34 (3, 24879.45) 231.28 (3, 17868.11) 249.85 (3, 21326.61) D 4013.23 (3, 18064.26) 2570.75 (3, 17484.25) 6248.21 (3, 18068.64) 1829.03 (3, 18537.10) M 34.15 (2, 21571.44) 103.02 (2, 21102.18) 75.84 (2, 16417.49) 68.43 (2, 19090.67) S*D 19.71 (9, 18614.53) 11.82 (9, 18401.97) 9.60 (9, 17774.22) 60.09 (9, 18451.59) S*M 28.90 (6, 20170.81) 13.17 (6, 20142.66) 23.57 (6, 14309.88) 5.54 (6, 17071.21) D*M 42.28 (6, 14530.87) 57.75 (6, 14186.86) 44.35 (6, 14467.44) 13.43 (6, 14855.56) S*D*M 22.67 (18, 17021.57) 18.33 (18, 16949.97) 24.57 (18, 16181.22) 56.38 (18, 16805.40) Within-subjects E 14355.94 (3, 16937.01) 13654.15 (3, 15881.56) 12849.56 (3, 13520.35) 6768.33 (3, 15521.07) E*S 477.94 (9, 20805.45) 402.60 (9, 20286.70) 1146.01 (9, 18049.14) 551.36 (9, 19104.87) E*D 11663.55 (9, 14485.95) 10329.60 (9, 13932.84) 14093.12 (9, 13417.50) 2848.15 (9, 15184.48) E*M 196.35 (6, 14082.87) 195.18 (6, 13308.35) 546.87 (6, 11479.07) 425.92 (6, 12913.51) E*S*D 133.87 (27, 21842.24) 96.65 (27, 21851.62) 241.73 (27, 21595.30) 29.38 (27, 22533.00) E*S*M 51.77 (18, 19681.63) 46.87 (18, 19066.28) 171.71 (18, 17130.85) 92.77 (18, 17868.57) E*D*M 135.32 (18, 13767.40) 242.49 (18, 13309.31) 250.44 (18, 12822.42) 53.21 (18, 14338.56) E*S*D*M 36.30 (54, 22217.71) 22.02 (54, 22209.29) 71.59 (54, 21709.12) 19.90 (54, 22375.98) Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, and : factor correlation.

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83 Table 3-24. Eta squared for signi ficant effects on standard erro r ratio estimates: scale set by specifying factor loadings equal to one. Parameter (0.7/0.6) (0.8/0.6) (i,i) = (0.36) (i,j) = (0.108) Between-subjects S 0.0094 0.0086 0.0093 0.0150 D 0.1576 0.1139 0.1789 0.0910 M 0.0009 0.0029 0.0016 0.0020 S*D 0.0012 0.0014 0.0005 0.0049 S*M 0.0020 0.0013 0.0008 0.0004 D*M 0.0036 0.0045 0.0035 0.0013 S*D*M 0.0041 0.0035 0.0031 0.0086 Within-subjects E 0.0421 0.0364 0.0385 0.0153 E*S 0.0021 0.0025 0.0015 0.0019 E*D 0.1098 0.0898 0.1415 0.0486 E*M 0.0003 0.0005 0.0002 0.0005 E*S*D 0.0012 0.0013 0.0005 0.0005 E*S*M 0.0002 0.0002 0.0002 0.0002 E*D*M 0.0008 0.0019 0.0006 0.0003 E*S*D*M 0.0003 0.0003 0.0002 0.0001 Note. S indicates Sample size, D: distributi on, M: model, and E: estimation method. indicates factor loading, and : factor correlation. Table 3-25. Mean estimates of standard error ratio for factor loading parameters by parameter value, estimation method, and distribution. Distribution Parameter value Estimation (0, 0) (0, -1.15) (0, 3) (2, 6) 0.7/0.6 GLS 0.964 1.040 0.812 0.670 ML 0.989 1.073 0.824 0.673 Robust GLS 0.959 0.964 0.944 0.937 Robust ML 0.983 0.993 0.960 0.953 0.8/0.6 GLS 0.959 1.019 0.825 0.685 ML 0.987 1.048 0.839 0.692 Robust GLS 0.954 0.953 0.946 0.937 Robust ML 0.981 0.978 0.964 0.957 Table 3-26. Marginal means by sample size for standard error ratio estimates of (i, j) = 0.108 Sample sizeMarginal means of standard error ratio 2000.921 4000.9618000.96412000.975

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84 Table 3-27. Mean estimates of standard erro r ratio for factor correlation parameters by parameter value, estimation method, and distribution. Distribution Parameter value Estimation (0, 0) (0, -1.15) (0, 3) (2, 6) (i, j) = 0.108 GLS 0.976 1.022 0.921 0.795 ML 0.994 1.043 0.938 0.807 Robust GLS 0.971 0.982 0.960 0.945 Robust ML 0.988 1.003 0.978 0.964 (i, i) = 0.36 GLS 0.969 1.072 0.821 0.672 ML 0.993 1.100 0.832 0.675 Robust GLS 0.963 0.966 0.950 0.948 Robust ML 0.986 0.990 0.965 0.964 Table 3-28. Frequency of non-convergence (N C), improper estimates (IE), and non-positive definite Hessians (NP) Model Distribution Sample size Scale set Estimation NC IE or NP (0, 3) 200 Loading ML/RML 0.1% 0.0% 200 Variance ML/RML 0.2% 0.0% 3F3I (2, 6) 400 Loading ML/RML 0.1% 0.0% 200 Loading ML/RML 0.2% 0.0% (0, -1.15) 200 Loading GLS/RGLS 0.0% 0.1% 200 Loading ML/RML 0.8% 0.5% 200 Loading GLS/RGLS 0.2% 0.1% (0, 3) 400 Loading ML/RML 0.1% 0.1% 200 Loading ML/RML 0.4% 0.3% 200 Loading GLS/RGLS 0.1% 1.3% 3F6I (2, 6) 400 Loading ML/RML 0.1% 0.1% 200 Variance GLS/RGLS 0.0% 0.5% 6F3I (2, 6) 200 Loading GLS/RGLS 0.1% 0.2%

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85 CHAPTER 4 SUMMARY AND CONCLUSIONS For each of four estim ation methods (ML, GLS, Robust ML, and Robust GLS) the behavior of standard error ratio estimates was examined under each combination of four distributions ([0, 0], [0, -1.15], [0, 3], and [2, 6]), four sample sizes (200, 400, 800, and 1200), three CFA models (3F3I, 3F6I, and 6F3I), a nd two scale-setting methods (set by specifying factor variances equal to one a nd factor loadings equal to one) In addition, the bias of the parameter estimation was investigated since esti mation bias might have varied over estimation methods at some sample sizes and there might have be an interactive eff ect of estimation method with other factors. The effects of four factors (estimation method, distribution, model, and sample size) on parameter estimates and standard error estimates were examined within each scale-setting method. Comparison of Findings with Previous Studies Parameter Estimates Recall that ML and Robust ML produce the same parameter estimates as do GLS and Robust GLS, so for bias only a comparison of the ML and GLS estimation procedures was necessary. Important effects of factors were define d as those that were significant by the WJ test and accounted for at least 1% of the total sum of squares in a repeated measures ANOVA of the data. When the scale was set by specifying factor variances equal to one no factor had an important effect on estimation of the factor correlation and only estimation method had an important effect on estimates of the factor lo ading and then only when the population factor loading was 0.8. ML estimates had less bias than GLS estimates, but the effect was very small. The interaction of estimation method and sample si ze had an important effect on estimates of the residual variance. The results showed that ML es timates had minimal bias at all sample sizes.

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86 Therefore, although the bias of the ML estimates declined as samp le size increased, the effect of sample size on bias of ML estimates was quite small. The bias of GLS estimates also declined as sample size increases. Compared to the bias of ML estimates, the bias of GLS estimates was larger with larger differences between the me thods when the sample size was 200 or 400 and a trivial difference when the sample size was1200. When the scale was set by specifying factor loadings equal to one, most effects on parameter estimates of factor loading and factor correlation were quite small and none of them accounted for even 1% of the total sum of square s. Recall that estimates of residual variance are the same for both scale-setting methods. Therefore effects of the factors on the residual variance were the same for both methods of setting the scale. Boomsma (1983) indicated that, when the dist ribution is normal and the scale is set by specifying factor loadings equal to one, the bias of ML parameter estimates is small for models with more indicators per factor and higher factor loadings co mpared with the bias of ML estimates for models with fewer indicators per fa ctor and smaller factor loadings. Boomsma only investigated models for which the scale was set by specifying factor loadings equal to one. Muthen and Kaplan (1985) found that there was not much difference between ML and GLS parameter estimates. Henly (1993) also pointed out the similarity between ML and GLS, that is, equivalent conclusion was drawn about the behavi or of the ML and GLS parameter estimates in the distribution and sample size conditions studied: Like the ML parameter estimates, the GLS estimates appeared to produce consistent parameter estimates that are unbiased when N 600 for samples from the multivariate normal and asy mmetric multivariate populations (Henly, 1993). According to Hoogland and Boomsma (1998), the bias of ML parameter estimates increases when the levels of univariate skewness and kurto sis deviate increasingly from those of a normal

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87 distribution and a larger samp le size is a remedy to obtain unbiased parameter estimates. Hoogland (1999) reported that ML parameter esti mates were almost unbiased when the sample size was at least 200 and GLS had a much larger bias than ML. For parameter estimates, the overall results of the present study are consistent with the results of the previous studies: ML estimates were almost unbiased at all sample sizes and ML estimation had less bias than GLS estimation, although the differences were trivial for factor loadings. Sample size played more a critical ro le in GLS estimation than in ML estimation of residual variance and, as a result, larger between-method differenc es in bias were observed for estimates of residual variance. Wh en the scale was set by specifying factor loadings equal to one, there were no important effects of the factors on the factor loading, fact or variance, or factor covariance estimates. Standard Error Estimates When the scale was set by specifying factor va riances equal to one, the bias of standard error estimates for the factor loadings decr eased as the sample size increased, showing consistency with theory that standard error esti mates will converge to their true values as the sample size increases without bound. For factor lo adings, the ML standard error estimates had minimal bias for the normal distribution and Robust ML estimates had a relatively small bias regardless of the distribution. Al so, there was relatively little di fference between the biases of ML and GLS estimates of standard errors, and rela tively little difference between the biases of Robust ML and Robust GLS estimates of standard errors regardless of the distribution. ML and GLS estimates of standard errors of factor load ing were overestimated when the distribution was short-tailed (0, -1.15) and strongl y underestimated when the distri butions were long-tailed (0, 3) and long-tailed and skewed (2, 6).

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88 A long tail to the distribution tended to result in underestimation of standard errors of the inter-factor correlation and underestim ation was particularly large fo r the (2, 6) distribution. For the (2, 6) distribution, bias of the standard er rors decreased systematically as the sample size increased. For the other distributions, estimates of the standard error ratio did not systematically decline as the sample size increased because the empirical standard error estimates and standard error estimates for the factor correlation decreased as the sample size increased but not the same rate (please see Appendix A). Wh en the distribution was normal, (0, -1.15) and (0, 3), ML and Robust ML estimation of standard errors were less biased than were GLS and Robust GLS estimation of standard errors. The robust esti mation procedures showed substantially better performance than the non-robust estimation procedures for the (2, 6) distribution. The bias of the standard error estimates for residual variance decreased as the sample size increased regardless of the population value. The bias increased as the degree of non-normality increased regardless of the model type. For a normal distribution, more indicators per factor resulted in less bias, but resulted in more bi as for the non-normal distributions. For a normal distribution, ML and Robust ML estimation performed better than GLS and Robust GLS estimation of the standard error estimates of re sidual variances, whereas the robust procedures performed better than the non-robus t procedures for the non-normal di stributions. The bias of the standard error estimates for residual variances te nded to decrease as the population value (0.64, 0.51, and 0.36) decreased. When the scale was set by specifying factor lo adings equal to one a nd the distribution was normal, between-estimation method differences in th e bias of the standard error estimates for the factor loadings were very sma ll. For the non-normal distributions robust estimation of standard errors of factor loading performed better than did non-robust estimation. When the distribution

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89 was (0, -1.15), the standard error estimates for the factor loadings tended to be overestimated by the non-robust estimation procedures and undere stimated by the robust estimation procedures. For the other distributions, the standard error estimates of the factor loadings tended to be underestimated regardless of the estimation procedures. The bias of standard error estimates for the factor covariances ( (i, j) = 0.108) tended to decrease as the sample size increased. For the normal distribution, ML and Robust ML estimates of the standard errors for the factor covariance had less bias than did the GLS and Robust GLS estimates. The robust procedures had less bias than the non-robust procedures for the non-normal distributions. Robust ML estimates of the standard errors for the factor correlations had the minimal bias for the non-normal distributions. Chou, Bentler, and Satorra (1991) studied th e performance of Robust ML estimates of standard errors and found that wh en the distribution had excessive kurtosis, the robust estimates of standard errors were superi or to ML estimates. Finch, We st, and MacKinnon (1997) indicated that the Robust ML estimates of standard erro rs provided more accurate estimates of sampling variability than ML and GLS as non-normality increased, and the standard error estimates generated by ML and GLS were likely to be too small. Hoogland (1999) poi nted out that the ML and GLS estimates of the standard errors were biased when the average kurtosis of the observed variables deviates from zero, th e standard error estimates were underestimated in the case of a positive average kurtosis and overestimated in the case of negative average kurtosis. Also Hoogland (1999) reported that the Robust ML standard error estimat es had a smaller bias than ML and GLS standard error estimates when the av erage kurtosis was at least 2.0 and the sample size was at least 400.

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90 The results of the present study generally s howed consistency with the results of the previous studies in that Robust ML estimates we re superior to the non -robust estimates in the bias of the standard error estimates for the non-normal distributions, and the standard error estimates were underestimated for the distributi on with positive kurtosis and overestimated for the distribution with negative kurtosis. However, the present study gives more general results because Robust GLS was added, two scale-setting methods were compared, and results were analyzed statistically. Brief Summary The main purpose of the present study was to answer the question: Which estimation method provides better standard errors? A Monte Carlo simulation study was conducted to analyze and investigate the effect s of four factors (estimation, sample size, distribution, and model) on the bias of standard er ror estimates and the bias of parameter estimates in each scalesetting method. First, from the findings of the study, the followi ng conclusions can be set forth in regard to for the bias of parameter estimates: When the scale is set by specifying factor va riances equal to one, ML estimates of the factor loading have less bias than do the GLS estimates. The bias of ML and GLS estimates of residual variance decreases as the sample size increases and the bias of GLS estimation is mo re affected by sample sizes between 200 and 1200 than is ML estimation. The ML estimates of residual variance have mi nimal bias at all sample sizes whereas the GLS estimates have more substantial bias when sample size is 200 or 400. When the scale is set by specifying factor load ings equal to one, none of the effects on the factor loading or the f actor correlation paramete r estimates account for even 1% of the total sum of squares.

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91 Second, the following points can be noted about the bias of standard error estimates: In regard to bias of the standard error estim ators for factor loadings: Regardless of scalesetting method, for a normal distribution, ML es timation is superior to the other estimation procedures, whereas for non-normal distributi ons, Robust ML is superior to the other estimation procedures. In regard to bias of the standard error estimators for the factor correlation: 1. When the scale is set by specifying factor variances equal to one, the mean standard error estimates and the empiri cal standard error estimates decrease as the sample size increases, regardless of the m odel type, but not the same rate. 2. When the scale is set by specifying factor variances equal to one, ML and Robust ML are superior to GLS and Robust GLS for the (0, 0), (0, -1.15), (0, 3) distributions, whereas Robust ML and Robust GLS are superior to ML and GLS for the (2, 6) distribution. 3. When the scale is set by specifying factor loadings equal to one, ML and Robust ML are superior to GLS and Robust GLS for th e (0, 0) distribution, whereas Robust ML and Robust GLS are superior to ML and GL S for the (0, -1.15), (0, 3), and (2, 6) distributions In regard to the bias of standard error es timates for residual variance: ML and Robust ML are superior to GLS and Robust GLS for a normal distribution, wh ereas Robust ML and Robust GLS are superior to ML and GLS for non-normal distributions The bias of standard error estimates for re sidual variance increases as the number of indicators per factor increases for non-norma l distributions, whereas the bias of these estimates decreases as the number of indi cators per factor increases for a normal distribution. Concluding Remarks Based on the findings presented, it can be conc luded that ML estimation method should be adopted for a normal distribution re gardless of sample size, mode l, and scale-setting method to obtain less biased estimates of parameters and standard errors, and Robust ML should be used for non-normal distributions to improve estimati on of standard errors. However, Robust ML estimation works very well even for normal distribut ions and some cases better than GLS. It has also been found that robust estimation generally worked better than non-robust estimation for the non-normal distributions regardless of the sample size and the model type. When the distribution

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92 is non-normal, Robust GLS generally performs well, although Robust ML has less bias than Robust GLS. Problems of non-convergence, improper estimates, and non-positive definite Hessian matrices were more common when the scale was set by specifying factor loadings equal to one and the distribution is non-normal, particularly for the 3F6I model with 200 and 400 sample sizes. However, as a practical matter these prob lems occurred infrequently. Setting the scale by specifying factor variances equal to one s hould be chosen to avoid these problems. Generalization from the findings of Monte Carl o simulation studies is limited by the design of the simulation. Although the present study evaluated the empi rical behavior of parameter estimates and standard error estimates under a ra nge of sample sizes, di stributions, and models which were chosen considering the results of the previous studies, the study of the robust estimation for standard errors st ill needs to be investigated further under the various conditions of models (e.g., misspecified models, or hybrid models), and the other robust estimation procedures (e.g., Robust DWLS, or Robust ULS).

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93 APPENDIX STANDARD ERROR RESULTS FOR FACTOR CORRELATIONS: SCALE SET BY SPECIFYING FAC TOR VARIANCES EQUAL TO ONE. Table A-1. Mean standard errors, empirical sta ndard errors, and standard error ratios by model, distribution, and sample size for ML estimation. Sample size Model Distribution Statistics 200 400 800 1200 SE 0.08520350.06072740.04309100.0351752 Emp. SE 0.09165270.05986770.04482500.0341341 (0, 0) SE Ratio 0.92963461.01435860.96131801.0305009 SE 0.08534020.06050110.04298520.0351710 Emp. SE 0.08947830.06067890.04257630.0361427 (0, -1.15) SE Ratio 0.95375300.99707031.00960330.9731165 SE 0.08507280.06057910.04291770.0351232 Emp. SE 0.08920470.06091620.04414730.0365462 (0, 3) SE Ratio 0.95368060.99446620.97214770.9610636 SE 0.08438460.06045250.04288930.0351204 Emp. SE 0.10150780.07197240.04996870.0385356 3F3I (2, 6) SE Ratio 0.83131140.83994040.85832330.9113744 SE 0.07477180.05298570.03755880.0306787 Emp. SE 0.07708730.05179380.03959370.0305296 (0, 0) SE Ratio 0.96996371.02301100.94860421.0048813 SE 0.07479550.05291230.03751410.0306708 Emp. SE 0.07845400.05178730.03689420.0293325 (0, -1.15) SE Ratio 0.95336791.02172361.01680201.0456226 SE 0.07483600.05305620.03751880.0306476 Emp. SE 0.07642600.05344270.03722200.0299660 (0, 3) SE Ratio 0.97919600.99276871.00797291.0227563 SE 0.07454670.05286500.03746070.0305994 Emp. SE 0.08447310.05994500.04281350.0352090 3F6I (2, 6) SE Ratio 0.88249070.88189200.87497370.8690774 SE 0.08552090.06065820.04300080.0351380 Emp. SE 0.08872590.06026600.04114500.0353648 (0, 0) SE Ratio 0.96387731.00650751.04510380.9935871 SE 0.08500980.06063010.04300290.0351318 Emp. SE 0.08910510.06088260.04409000.0346944 (0, -1.15) SE Ratio 0.95403980.99585300.97534411.0126064 SE 0.08509000.06060050.04299200.0351811 Emp. SE 0.08430960.06253060.04452360.0356247 (0, 3) SE Ratio 1.00925620.96913200.96559820.9875455 SE 0.08473800.06038360.04295980.0351398 Emp. SE 0.10112570.06728430.04777660.0382489 6F3I (2, 6) SE Ratio 0.83794760.89743970.89918090.9187121 Note SE indicates standard error, and Emp. Indicates empirical.

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94 Table A-2. Mean standard errors, empirical sta ndard errors, and standard error ratios by model, distribution, and sample size for GLS estimation. Sample size Model Distribution Statistics 200 400 800 1200 SE 0.08654750.06118730.04325700.0352676 Emp. SE 0.09472460.06049010.04541830.0343848 (0, 0) SE Ratio 0.91367541.01152670.95241251.0256748 SE 0.08666790.06096040.04314560.0352652 Emp. SE 0.09168330.06188600.04290620.0362790 (0, -1.15) SE Ratio 0.94529640.98504311.00557980.9720555 SE 0.08632690.06108730.04308760.0352161 Emp. SE 0.09329350.06162070.04441690.0369437 (0, 3) SE Ratio 0.92532580.99134420.97007120.9532371 SE 0.08604430.06106680.04311840.0352416 Emp. SE 0.10668390.07347570.05041000.0387986 3F3I (2, 6) SE Ratio 0.80653520.83111500.85535380.9083230 SE 0.07847970.05426360.03803050.0309424 Emp. SE 0.08520810.05415150.04055040.0308609 (0, 0) SE Ratio 0.92103501.00207090.93785691.0026409 SE 0.07855640.05426920.03798420.0309353 Emp. SE 0.08598180.05416450.03818440.0299032 (0, -1.15) SE Ratio 0.91363991.00193470.99475681.0345156 SE 0.07850630.05438380.03798840.0309098 Emp. SE 0.08408020.05630510.03803010.0303428 (0, 3) SE Ratio 0.93370760.96587630.99890331.0186850 SE 0.07903100.05451710.03805040.0309130 Emp. SE 0.09529440.06383070.04430690.0361298 3F6I (2, 6) SE Ratio 0.82933490.85408900.85879200.8556083 SE 0.08787150.06157710.04333240.0353174 Emp. SE 0.09509060.06288970.04196380.0357739 (0, 0) SE Ratio 0.92408180.97912841.03261450.9872395 SE 0.08745930.06158800.04333570.0353145 Emp. SE 0.09657760.06315810.04527860.0352822 (0, -1.15) SE Ratio 0.90558570.97513980.95709031.0009153 SE 0.08761330.06158880.04334810.0353710 Emp. SE 0.08977610.06540290.04579400.0357338 (0, 3) SE Ratio 0.97590860.94168390.94658950.9898479 SE 0.08803380.06156640.04338580.0353801 Emp. SE 0.10905500.07061360.04862420.0390347 6F3I (2, 6) SE Ratio 0.80724260.87187750.89226790.9063758

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95 Table A-3. Mean standard errors, empirical sta ndard errors, and standard error ratios by model, distribution, and sample si ze for Robust ML estimation. Sample size Model Distribution Statistics 200 400 800 1200 SE 0.08415640.06037120.04295780.0350993 Emp. SE 0.09165270.05986770.04482500.0341341 (0, 0) SE Ratio 0.91821001.00840950.95834571.0282767 SE 0.08508320.06057740.04310900.0352981 Emp. SE 0.08947830.06067890.04257630.0361427 (0, -1.15) SE Ratio 0.95088080.99832701.01251150.9766321 SE 0.08381080.05999010.04305420.0351824 Emp. SE 0.08920470.06091620.04414730.0365462 (0, 3) SE Ratio 0.93953300.98479700.97523970.9626822 SE 0.09193800.06730810.04826950.0397790 Emp. SE 0.10150780.07197240.04996870.0385356 3F3I (2, 6) SE Ratio 0.90572360.93519280.96599511.0322664 SE 0.07408260.05268490.03742710.0306684 Emp. SE 0.07708730.05179380.03959370.0305296 (0, 0) SE Ratio 0.96102271.01720360.94527831.0045460 SE 0.07451190.05294340.03759160.0307174 Emp. SE 0.07845400.05178730.03689420.0293325 (0, -1.15) SE Ratio 0.94975281.02232451.01890481.0472134 SE 0.07315630.05262140.03743050.0306659 Emp. SE 0.07642600.05344270.03717850.0299660 (0, 3) SE Ratio 0.95721800.98463241.00677701.0233568 SE 0.08184130.05874170.04229600.0346940 Emp. SE 0.08447310.05994500.04281350.0352090 3F6I (2, 6) SE Ratio 0.96884510.97992700.98791280.9853713 SE 0.08457480.06026010.04289380.0350724 Emp. SE 0.08872590.06026600.04114500.0353648 (0, 0) SE Ratio 0.95321510.99990201.04250490.9917328 SE 0.08468870.06065850.04310390.0352690 Emp. SE 0.08910510.06088260.04409000.0346944 (0, -1.15) SE Ratio 0.95043650.99631870.97763551.0165624 SE 0.08332260.06045040.04312050.0352033 Emp. SE 0.08430960.06253060.04452360.0356247 (0, 3) SE Ratio 0.98829310.96673240.96848460.9881700 SE 0.09221640.06713130.04830980.0399671 Emp. SE 0.10112570.06728430.04777660.0382489 6F3I (2, 6) SE Ratio 0.91189930.99772621.01116121.0449214

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96 Table A-4. Mean standard errors, empirical sta ndard errors, and standard error ratios by model, distribution, and sample size for Robust GLS estimation. Sample size Model Distribution Statistics 200 400 800 1200 SE 0.08548790.06083520.04312660.0351923 Emp. SE 0.09472460.06049010.04541830.0343848 (0, 0) SE Ratio 0.90248941.00570610.94954341.0234863 SE 0.08643080.06104560.04326990.0353919 Emp. SE 0.09168330.06188600.04290620.0362790 (0, -1.15) SE Ratio 0.94271070.98642041.00847530.9755476 SE 0.08509320.06053070.04323720.0352852 Emp. SE 0.09329350.06162070.04441690.0369437 (0, 3) SE Ratio 0.91210260.98231050.97343960.9551081 SE 0.09391000.06814300.04859030.0399571 Emp. SE 0.10668390.07347570.05041000.0387986 3F3I (2, 6) SE Ratio 0.88026360.92742100.96390221.0298613 SE 0.07763600.05393500.03788680.0309388 Emp. SE 0.08520810.05415150.04055040.0308609 (0, 0) SE Ratio 0.91113330.99600180.93431221.0025226 SE 0.07826400.05428980.03807180.0309850 Emp. SE 0.08598180.05416450.03818440.0299032 (0, -1.15) SE Ratio 0.91023981.00231410.99705101.0361790 SE 0.07680610.05398440.03792510.0309261 Emp. SE 0.08408020.05630510.03806410.0303428 (0, 3) SE Ratio 0.91348650.95878320.99634991.0192230 SE 0.08673320.06070910.04307800.0350903 Emp. SE 0.09529440.06383070.04430690.0361298 3F6I (2, 6) SE Ratio 0.91016060.95109570.97226290.9712282 SE 0.08699080.06116490.04323450.0352456 Emp. SE 0.09509060.06288970.04196380.0357739 (0, 0) SE Ratio 0.91482020.97257341.03028020.9852329 SE 0.08706430.06163830.04344500.0354532 Emp. SE 0.09657760.06315810.04527860.0352822 (0, -1.15) SE Ratio 0.90149620.97593580.95950311.0048462 SE 0.08580120.06146860.04346880.0354037 Emp. SE 0.08977610.06540290.04579400.0357338 (0, 3) SE Ratio 0.95572390.93984580.94922610.9907638 SE 0.09543630.06820840.04869650.0401757 Emp. SE 0.10905500.07061360.04862420.0390347 6F3I (2, 6) SE Ratio 0.87512120.96593791.00148641.0292304

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103 BIOGRAPHICAL SKETCH Youngkyoung Min was born in Seoul, South Kor ea. She received her bachelors and m asters degrees in polymer science and engi neering from Chungnam National University, South Korea. She was an engineering researcher of the research and devel opment center in Kumho Chemical Inc. In August 2001, she completed her M. S. in Management at the University of Florida, and she also achieved he r M. S. in Statistics at the Univ ersity of Florida in August 2004. In the fall of 2004, she enrolled for graduate studies in research and evaluation methodology program of Educational Psychology Department at the University of Florida and completed her Ph.D. in research and evaluation me thodology in August 2008. Youngkyoung Min conducted researches as a research assistant at the Depart ment of Health and Clinical Psychology at the University of Florida from August 2004 to August 2005, and in Engineering Education Center of College of Engineering at the University of Florida from August 2005 to June 2008.