Yeni LISREL Eğitim İçeriği - Online

 Yeni dönemde internet üzerinden LISREL eğitimlerine başlıyoruz. Revize ettiğimiz yeni programın içeriği:

Preliminaries

• LISREL software installation
• PRELIS
• Data entry and Data Edit issues
• Correlation and Covariance Data Files

Modeling

• SEM Basics
• Regression models
• Diagramming Models
• Path Analysis Models

Measurement Models

• Exploratory vs. Confirmatory factor analysis
• Latent Variables
• CFA models

Developing Structural Equation Models

• Combining Path and Factor Models
• 5 Basic SEM steps
  • Model Specification
  • Model Identification
  • Model Estimation
  • Model Testing
  • Model Modification

Yapısal Eşitlik Modellemesi

Yapısal Eşitlik Modellemesi

Structural Equation Modeling on the Internet

Congruent with its purpose, the Structural Equation Modeling SIG of AERA maintains an index of SEM-related resources available on the WWW. If you are maintaining a WWW site related to structural equation modeling, please send the URL to Greg Hancock, Chair, AERA SIG: Structural Equation Modeling, to be considered for inclusion in this listing.

Karl G. Jöreskog: New Research Paper

C. Van Tuyckom, & K. Jöreskog, “Going for gold! Welfare characteristics and Olympic success: an application of the structural equation approach.” Quality & Quantity (in press)

Winning medals at the Olympic Games has become an objective that countries worldwide want to achieve. In line with research devoted to the predictors of success, the present article examines the connection between certain welfare characteristics (political, social, and economic development) and the probability of success in the 1984 and 2004 Olympics. We expected to find that structural macro conditions still predict Olympic success. Using welfare indicators as formative instead of reflective indicators (Bollen, Qual Quant 183: 77–85, 1984, p. 65), the results of our Structural Equation Model reveal that both economic and social development had an effect in 1984, as well as in 2004. Political development was only significant in 1984. As for the control variables, population size was significant in both 1984 and 2004. Sporting tradition and geographical conditions had no effect at all. The model fit is very good with a chi-square of 6.62 with 5 degrees of freedom (p =  0.25).

 

lavaan - latent variable analysis

What is lavaan?

lavaan is a free, open source R package for latent variable analysis. You can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.
The lavaan package is developed to provide useRs, researchers and teachers a free open-source, but commercial-quality package for latent variable modeling. The long-term goal of lavaan is to implement all the state-of-the-art capabilities that are currently available in commercial packages.

OpenMx - Advanced Structural Equation Modeling

What is OpenMx?

OpenMx is free and open source software for use with R that allows estimation of a wide variety of advanced multivariate statistical models. OpenMx consists of a library of functions and optimizers that allow you to quickly and flexibly define an SEM model and estimate parameters given observed data.

OpenMx runs on Mac OS X, Windows XP, Windows Vista, and several varieties of Linux. This means the same scripts you write in Windows will run in Mac OS X or Linux.

OpenMx can be used by those who think in terms of path models or by those who prefer to specify models in terms of matrix algebra. OpenMx is extremely powerful, taking full advantage of the R programming environment. This means that complicated models and data sets can be specified and modified using the R language. In order to give a very brief idea of what OpenMx looks like, here are two small demo examples: one from a path modeler's perspective and one from a matrix algebra perspective.

Path Model Specification

Here is a path diagram for a one factor path model with five indicators. Beside it is an R script using OpenMx path modeling commands to read the data from disk, create the one factor model, fit the model to the observed covariances, and print a summary of the results.

Van is Beautiful

Photo : Ali İhsan Öztürk

11th Annual IAS-STS Conference

"Critical Issues in Science and Technology Studies" May 7-8, 2012
Institute for Advanced Studies on Science, Technology and Society (IAS-STS) GRAZ - AUSTRIA

Special Session: Mobile learning: 

H. Eray Çelik  - A Semi-Structured Distant Education Practice towards Van Earthquake.   

LISREL Çalışma Notları

LISREL: ÖLÇÜM MODELİ VE YAPISAL MODELİNOLUŞTURULMASI

YEM’ de analiz aşamasında farklı iki yol izlenerek bütünleşik modelin uyumu ve ilgili testleri yapılmaktadır.  İki aşamalı ve tek aşamalı analiz yaklaşımı olarak tanımlanan bu yaklaşımlar, modelin bütünsel olarak nasıl analiz edileceğini açıklamaktadır. Tek aşamalı yaklaşımda önsel olarak oluşturulan kuramsal araştırma modelinin tüm unsurları (yapısal ve ölçüm kısımları) aynı anda analiz safhasına ilave edilerek YEM’ e ilişkin sonuçların tamamının elde edilmesi sağlanır. İki aşamalı yaklaşımda ise ölçüm ve yapısal model ayrı ayrı test edilmektedir. Bu yaklaşımda öncelikle ölçüm modeli, kabul edilebilir uyum değerlerini üretecek şekilde düzeltme ölçütleri kullanılarak geliştirilmeye çalışılmaktadır. Ölçüm modelinin uygunluğu istatistiksel olarak değerlendirildikten sonra yapısal modele ilişkin analizlerin yapılması için ikinci aşamaya geçilmektedir (Loehlin, 2004). İki aşamalı yaklaşımın ilk aşaması DFA olarak da ele alınabilir.  Öncelikle ölçüm modelinin istatistiksel uygunluğunun değerlendirilebilmesi için tam modelden başlayarak uygun modelin elde edilmesine kadar analizler yinelenir daha sonra yapısal model için gerekli işlemler gerçekleştirilir.

Kısa Çalışma Notları I

Stata- Structural equation modeling

SEM stands for structural equation modeling. SEM is a notation for specifying structural equations, a way of thinking about them, and methods for estimating their parameters.

SEM encompasses a broad array of models from linear regression to measurement models to simultaneous equations, including along the way confirmatory factor analysis (CFA), correlated uniqueness models, latent growth models, and multiple indicators and multiple causes (MIMIC).

Stata’s new sem command fits SEMs.

Features

• Use GUI or command language to specify model.
• Standardized and unstandardized results.
• Direct and indirect effects.
• Goodness-of-fit statistics.
• Tests for omitted paths and tests of model simplification including modification indices, score tests, and Wald tests.
• Predicted values and factor scores.
• Linear and nonlinear (1) tests of estimated parameters and (2) combinations of estimated parameters with CIs.
• Estimation across groups is as easy as adding group(sex) to the command. Test for group invariance. Easily add or relax constraints across groups.
• SEMs may be fitted using raw or summary statistics data.
• Maximum likelihood (ML) and asymptotic distribution free (ADF) estimation. ADF is also known as generalized method of moments (GMM). Missing at random (MAR) data supported via FIML.
• Robust estimate of standard errors and standard errors for clustered samples available.
• Support for survey data including sampling weights, stratification and poststratification, and clustered sampling at one or more levels.

http://www.stata.com/stata12/structural-equation-modeling/

Using Path Diagrams as a Structural Equation Modelling Tool

Linear structural equation models (SEMs) are widely used in sociology, econometrics, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error” or “disturbance” terms), and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. It is often thought that the path diagram is nothing more than a heuristic device for illustrating the assumptions of the model. However, in this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling.

Using Path Diagrams as a Structural Equation Modelling Tool

Essex Summer School in Social Science Data Analysis

1S Structural Equation Modelling with MPLUS

Tom Scotto, University of Essex
9 – 20 July (two week course / 35 hours)

Detailed Course Outline [PDF]

 

SmartPLS

SmartPLS is a software application for the modeling of structural equation models. 
SmartPLS