Structural Equation Modelling (SEM) dengan LISREL Konsep & Tutorial book . Read 3 reviews from the world's largest community for readers. Ada dua pe. buku tutorial lisrel ebook download. Quote. Postby Just» Tue Aug 28, 20 am. Looking for buku tutorial lisrel ebook download. Will be grateful for any. After registering, a video-based manual is available by entering this url in a web See the separate Statistical Associates “Blue Book” on “Weighted Least Squares”). Robustness testing of PLS, LISREL, EQS and ANN-based SEM for .
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International Standard Book Number: (Hardback) (Paperback) . LISREL–PRELIS Missing Data Example. User guide for use with the windows version of LISREL 8 for Windows 95, 98, NT, and Millenium and includes the Formal Preview this book». lesson that I learned years later with a blend of great disillusionment and enriching .. book about Partial Least Squares Path Modeling with R. . the overwhelming market share of LISREL, PLS-PM occupies a small niche within the SEM.
Disclaimer: This eBook does not include the ancillary media that was This book provides a basic introduction to structural equation modeling SEM. Goodreads helps you keep track of books you want to read. Want to Read saving…. Want to Read Currently Reading Read.
Other editions. Enlarge cover. Error rating book. A Festschrift in honor of Karl Joreskog. Hershberger, S. Some contributions to factor analysis Report No. Joreskog, K. Wijanto lisrel Statistical estimation in factor analysis: A new technique and its foundation.
A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, A general method for estimating a linear structural equation system. Duncan Eds. New York: Keesling, J. Maximum likelihood approaches to causal flow analysis. Unpublished doctoral dissertation, University of Chicago. Lawley, D. Estimation in factor analysis under various initial assumptions.
Karl Pearson. An appreciation of some aspects of his life and work. Biometrika, 29, Spearman, C. The proof and measurement of association between two things.
American Journal of Psychology, 15, The abilities of man. Wiley, D. The identification problem for structural equation models with unmeasured variables.
Wright, S. On the nature of size factors. Genetics, 3, Correlation and causation. Journal of Agricultural Research, 20, The method of path coefficients. Annals of Mathematical Statistics, 5, Latent variable: Observed variable: Dependent variable: Independent variable: Explain the difference between a dependent latent variable and a dependent observed variable: A dependent latent variable is not directly measured, but is computed using multiple dependent observed variables.
A dependent observed variable is a raw score obtained from a measurement instrument or assigned to a criterion variable. Explain the difference between an independent latent variable and an independent observed variable: An independent latent variable is not directly measured, but is computed using multiple independent observed variables. An independent observed variable is a raw score obtained from a measurement instrument or assigned to an attribute variable.
Researchers are becoming more aware of the need to use multiple observed variables to better understand their area of scientific inquiry.
More recognition is given to the validity and reliability of observed scores from measurement instruments. Structural equation modeling has improved recently, especially the ability to analyze more advanced statistical models. SEM software programs have become increasingly user-friendly.
Other important steps involve being able to use the SEM software programs' saved file system file , and output and save files that contain the variance-covariance matrix, the correlation matrix, means, and standard deviations of variables so they can be input into command syntax programs, for example, EQS and SIMPLIS. There are several key issues in the field of statistics that impact our analyses once data have been imported into a software program.
These data issues are commonly referred to as the measurement scale of variables, restriction in the range of data, missing data values, outliers, linearity, and nonnormality.
Each of these data issues will be discussed because they not only affect traditional statistics, but present additional problems and concerns in structural equation modeling. We use Amos, EQS, and LISREL software throughout the book, so you will need to use their software programs and become familiar with their websites, and should have downloaded a free student version of their software. We also use some of the data and model examples available in their free student versions to illustrate SEM applications.
The free student versions of the software have user guides, library and help functions, and tutorials. The websites also contain important research, documentation, and information about structural equation modeling.
However, be aware that the free student versions of the software do not contain the full capabilities for importing data, data analysis, and computer output available in their full product versions. If you hold the mouse over any of the toolbox icons it will provide a basic description of what it does.
Data files are imported by using the File option on the pull-down menu. Now click on File Name to open the second dialog box to browse for a data file.
Amos interfaces with SPSS to make it easier to enter raw data and label variable names and variable values for file input into Amos. SPSS can also be used to edit data for missing values, outliers, linearity, and nonnormality of variable values as discussed in this chapter. It is recommended that raw data be input and saved as an EQS system file for future use; and also be saved as an SPSS save file for input into another program.
When clicking on Open, you can browse to find either a saved EQS system file, for example, airpoll. EQS uses a spreadsheet when viewing data, creates and saves its own system files, and offers menu options for editing data, handling missing values, identifying outliers, checking linearity, and testing for nonnormality in data values.
For example, the EQS system file airpoll.
The raw data file lsat6. When selecting this file, you will need to know the number of variables in the file. Other important data editing features include imputing missing values, a homogeneity test, creation of normal scores, bootstrapping, and data output options.
This capability is very important, especially when advanced SEM models are analyzed in chapters 13 and Properties of scale also guide our understanding of permissible mathematical operations.
For example, a nominal variable implies mutually exclusive groups; for example, gender has two mutually exclusive groups, male and female. An individual can only be in one of the groups that define the levels of the variable. In addition, it would not be meaningful to calculate a mean and a standard deviation on the variable gender. Consequently, the number or percentage of individuals at each level of the gender variable is the only mathematical property of scale that makes sense.
An ordinal variable, for example, attitude toward school, that is scaled strongly agree, agree, neutral, disagree, and strongly disagree implies mutually exclusive categories that are ordered or ranked.
When levels of a variable have properties of scale that involve mutually exclusive groups that are ordered, only certain mathematical operations are meaningful, for example, a comparison of ranks between groups. An interval variable, for example, continuing education credits, possesses the property of scale implying equal intervals between the data points, but no true zero point. This property of scale permits the mathematical operation of computing a mean and a standard deviation.
Similarly, a ratio variable, for example, weight, has the property of scale that implies equal intervals and a true zero point weightlessness. Therefore, ratio variables also permit mathematical operations of computing a mean and a standard deviation.
Our use of different variables requires us to be aware of their properties of scale and what mathematical operations are possible and meaningful, especially in SEM, where variance-covariance correlation matrices are used with means and standard deviations of variables.
Different correlations among variables are therefore possible depending upon the level of measurement, but create unique problems in SEM see chap. Restriction of Range Data values at the interval or ratio level of measurement can be further defined as being discrete or continuous.
For example, the number of continuing education credits could be reported in whole numbers discrete. In contrast, a continuous variable is reported using decimal places; for example, a students' grade point average would be reported as 3. Joreskog and Sorbom provided a criterion in the PRELIS program based on research that defines whether a variable is ordinal or interval based on the presence of 15 distinct scale points.
Other factors that affect the Pearson correlation coefficient are presented in this chapter and discussed further in chapter 3. Missing Data The statistical analysis of data is affected by missing data values in variables. It is common practice in statistical packages to have default values for handling missing values.
The researcher has the options of deleting subjects who have missing values, replacing the missing data values, and using robust statistical procedures that accommodate for the presence of missing data. SEM software programs handle missing data differently and have different options for replacing missing data values.
Table 2. These options can dramatically affect the number of subjects available for analysis and the magnitude and the direction of the correlation coefficient, and can create problems if means, standard deviations, and correlations are computed based on different sample sizes. Listwise deletion of cases and pairwise deletion of cases are not always recommended due to the possibility of TABLE 2.
Mean substitution works best when only a small number of missing values is present in the data, whereas regression imputation provides a useful approach with a moderate amount of missing data. Amos uses full information maximum likelihood estimation in the presence of missing data, so it does not impute or replace values for missing data.
The value to be substituted for the missing value of a single case is obtained from another case that has a similar response pattern over a set of matching variables. In multivariable data sets, where missing values occur on more than one variable, one can use multiple imputation of missing values with mean substitution, delete cases, or leave the variables with defined missing values as options in the dialog box.
In addition, the multiple imputation procedure implemented in LISREL uses either the expected maximization EM algorithm or Monte Carlo Markov chain MCMC; generating random draws from probability distributions via Markov chains approaches to replacing missing values across multiple variables.
A beginner's guide to structural equation modeling We assume the data to be missing at random MAR with an underlying multivariate normal distribution. We must know the number of variables in the raw data file.
We must also select Data, then Define Variables, and then select We should examine our data both before Table 2. This provides us with valuable information about the nature of the missing data. We also highly recommend comparing SEM analyses before and after the replacement of missing data values to fully understand the impact missing data values have on the parameter estimates and standard errors.
A comparison of EM and MCMC is also warranted in multiple imputations to determine the effect of using a different algorithm for the replacement of missing values. We then provide a blueprint on how to apply each of these techniques in succession to understand and correct for diagnostic overlap of two or more conditions [ 2 , 3 ].
For each of these techniques the analyst must make a series of complex model decisions e. We provide details on a general sequence of models which can be used by working through the motivating example and using the MPlus software to show how to make such decisions based on theory and empirical evidence.
Modern SEM has progressed rapidly with many new developments appropriate for other settings not discussed in this tutorial i. Therefore, in order to give a clear overview, we focus on an important problem in the cross-sectional setting for our general discussion. We start, in Section 2, with background information about a motivating example. This is followed, in Section 3, with a basic introduction to the SEM framework. In Section 6 we provide an algorithm for adjusting a scale to isolate the latent dimension of the intended condition under study in the original scale along with practical applications of the adjusted scale for clinical use.
Section 7 summarizes the paper. We provide the MPlus code for our example in the Supporting Information available from the journal web page. Background information about motivating study 2. For example a depression screening scale may not accurately estimate depressed mood in a MS patient, due to overlapping symptoms of both conditions.
Researchers need reliable and accurate measures of symptoms or conditions that cannot be measured directly. Such measures that are unobserved are considered latent constructs. Unlike directly observable measures such as height or weight, researchers may not be able to measure variables such as depression directly.
To measure such a latent construct as depression, we can capture indicators from a multiple item scale such as the PHQ-9 that represent the underlying construct. These items are directly observed and in theory if we also account for additional measurement error in our construct, accurately represent the measure that cannot be observed directly. Patient-reported outcomes may similarly be used as a scale to help support treatment decision making through capturing an intended measure from the patient's perspective.
Patient-reported outcomes have an important place in psychiatry and neurology to measure the extent of disease or condition at the individual level, because they reflect the self-reported health state of the patient directly [ 14 ]. However, their effective integration in personalized medicine requires addressing certain conceptual and methodological challenges, since each individual patient may have a different view of how to fill out a test questionnaire, leading to a specific type of systematic error known as differential item functioning DIF.
This is different than our PRO definition in which we defined DIF based on some viewpoint that we don't specifically observe in our models. In the classic definition, DIF occurs when individuals from different groups e. This classic IRT type of DIF is also very important to account for when performing analysis of overlapping symptoms of co-occurring conditions. The results have led to mixed recommendations of whether certain items confound the depression screening scales.
For example, Mohr et al. In the analyses, the authors compared the PHQ-9 items for fatigue and poor concentration in MS patients to other subjects from the general population in the sample under study.
They concluded that there was no evidence to exclude these items from a modified PHQ-9 score. However, in this study we uniquely focus on a generalizable approach for determining the factor structure of a measure and then using this factor structure in a series of models to evaluate the overlap of multiple symptoms of multiple conditions simultaneously.
Our approach will then allow us to form practical adjusted scales which better represent single conditions individually, i. Further, our motivating study extends any prior work in the area by 1 using the largest observational cohort analyzed to date of only MS patients, eliminating the potential selection bias from a noncomparable control group 2 conducting psychometric analysis of the measurement properties of depression self-rating and MS disability measures.
While our motivating example focuses on the PHQ-9, it is straightforward to generalize to other scales. Multiple regression, path, and factor models are then reviewed and exploratory and confirmatory factor analysis is introduced.
These chapters demonstrate how observed variables share variance in defining a latent variables and introduce how measurement error can be removed from observed variables. Chapter 7 details the 5 SEM modeling steps including model specification, identification, estimation, testing, and modification along with a discussion of hypothesis testing and the related issues of power, and sample and effect sizes. Each of the 5 SEM modeling steps is explained for each model along with an application.
Chapter exercises provide practice with and enhance understanding of the analysis of each model. The book concludes with a review of SEM guidelines for reporting research. Designed for introductory graduate courses in structural equation modeling, factor analysis, advanced, multivariate, or applied statistics, quantitative techniques, or statistics II taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines.
Prerequisites include an introduction to intermediate statistics that covers correlation and regression principles. Reviews "Substantial improvements have been incorporated into this new edition, including a focus on individual SEM model applications and illustrations using multiple software platforms. This is a must own for novice and expert SEM users alike. The manual provides a step-by-step pragmatic approach to each type of model and offers extensive information on important issues and techniques not covered in most introductory SEM textbooks.
It effortlessly transitions the learner from the theory of SEM to its applications.