Lisrel and Eqs Structural Model Strategies - Essay Example

Structural Equation Modeling LISREL and EQS structural model strategies EQS is a structural model system, which is usually downloadable.it constitutes of special hardware and software solutions. The software solutions are tailor made for electronic manufacturing. Additionally, the system’s software solution enhances industrial automation and communication. The system boosts the test and measurement specialization .The structural model system ensures the working of the sensors and signal conditioning. It also helps in the operation of the calibration equipment. According to Hancock (2012), data acquisition is also enabling through the application of the system software solutions. The program is downloaded with the AMOS program, which helps in the articulation of the language used in the various applications. Moreover, instructions are downloaded in order for the users to apply the software and hardware solution of the model system correctly. The system applies various methods including the newly improved normal theory and the missing data methods. According to United States (2005), the case weighing methods and the arbitrary distribution methods are applied in order to obtain desired results. The system also incorporates the modeling features and the graphical user interface improvements. There are some tests undertaken in order to ensure that the system gives accurate results. According to Singer (2009), the nesting and equivalence tests are some of the tests applied in a bid to have a sound structural model application. The missing data method includes various procedures for multiple samples and homogeneity for the data means. The method also incorporates the internal consistency reliability and maximum reliability coefficients especially for the composite bias factor model. The method also makes use of advanced start values. The values help in the determination of the structured mean analysis. The corrected normal theory method makes use of satorra bentler information matrix for the LM test. The system’s arbitrary methods of distribution include the asymptomatically distribution free mean and the corrected chi-square tests. According to Fernando (2007), the user interface improvements entail the application of usable data editor with a big sample size. The project manager also ensures that all the information used for analysis is obtained from the tree view outline. The other user interface improvement measure is the use of a data sheet, which is modified with simple drag and drop operation. According to Birkhoff (2011), the application of the AMOS program comes in handy in the specification, estimation, assessment and presentation of the models. The system ensures that models are built more accurately with regard to the multivariate statistical techniques. The program users can choose to use the graphical user interface, the non-graphical user and the programmatic interface. According to Gill (2010), the structural system enables the building of behavioral models. The models indicate complex relationships. The AMOS software provides many forms of analysis including the structural equation model and the Bayesian analysis (Whicker, 2010). The equation model helps in the comparison, confirmation and definition of models. The Bayesian model enhances the improvement of the model parameters. The software also provides data imputation methods, which create varying data sets. Additionally, AMOS program builds the graphical models with the editing tools. The creation of models, which reflect the complexity of the relationships is ensured. According to Delaney (2011), the use of numeric values helps in the prediction of numeric values. The program also entails the use of graphical scripting capabilities in the running of large complicated models. It also incorporates the generation of related models. The program also takes advantage of the analysis of multi-variate variables. The aim of the analysis is to extend the standard methods of regression, factor analysis and correlation of variables. The application of the EQS structural system in AMOS program is evident in the improvement of estimates through the specification of informative distribution. According to Cassell (2009), the computational; methods used in the program enhance efficiency in the running of the program. The system also entails the performance of estimation with regard to categorical data. The strategy also offers many imputation methods like regression and stochastic regression imputation. The methods help in the creation of multiple imputed databases. LISREL The linear structural relation is a strategy based on the use of software package, which is concerned with statistical analysis. It is majorly applied in the structural equation modeling. Karl Joreskog (Samiec, 2012) established the software. The software is syntax-based and takes the form of the graphical user interface. The strategy enables researchers in various faculties to assess their hypothetical premises scientifically. The theories, which derive their application from the strategy, are also theoretically formulated. In case data is collected for the empirical variables, the LISREL program can help in articulating the model to compatible data (Guo, 2012). The software used in the modern times is not limited in its applications but it is applied across a wide array of applications. The applications applicable for this software include PRELIS and MULTILEV modeling. The former mode of modeling is only applicable for statistical analysis and manipulations of data. According to Dijkstra (2009), the multi-level modeling entails the hierarchical linear analysis. This software enhances the qualitative system coding. It helps in the analysis and evaluation of a programmer’s data given that it employs a sophisticated querying and visualization tools. It clears uncertainty in the programming process and gives insight into the variables subject to the configuration process. Moreover, it enables the sharing of data and testing of the validity and the relevance of the sets of data for programming (Bae, 2010). For instance, many pieces of text are placed in many different places in order to cover diverse types of aspects. The system shifting entails proper planning. This entails forecasting and implementing the action plans pertaining to the particular goal of an organization. The essential of the plan are ascertained before any action can be taken.it is important to carry out a strategic planning before dispensing any task. Operating system migration planning enhances efficiency and accuracy in the accomplishment of the desired goals (Hillary, 2010). The steps of planning include the determination of the current state, determination of the best plan of action, definition of the most essential goals and assign people tasks. The first step is crucial because it establishes the problems and the needs of the society prior to taking any form of action (Laurent, 2012). It is similar to a feasibility study in any project. In this respect, the management is able to obtain information on the affected area and the amount of resources required to accomplish the agenda (Bae, 2010). The next step is to identify the suitable plan of action. It is important t to have data in, as many codes as possible in order to have easy access to bulky data (Perez, 2012). Priori codes are used based on a theoretical framework. However, there is need to rely on the research question and related objectives. Additionally, themes of the data coded determine the data to be coded. The sophisticated software enables the generation of codes from the data to be coded. This is made possible using the informant words of the concerned data sets. According to Murray (2007), multiple coding is based on the tracks whereby broad codes are prioritized in the coding process. These are usually the general themes within the data. However, codes may overlap in this type of coding, which makes it inappropriate to use in coding process. This software enhances coding of fine and more specific codes with regard to the theoretical framework. This coding also depends on the complexity of the configuration and migration process. According to Baker (2011), multiple coding helps in case of polysomic meanings. In this case, the output of the specific code through which all the sections of the transcribed information is classified under just a specific like the gender aspect. The other sections that fall under the specific code are coded again in order to obtain smaller categories of data. Butler (2008) asserts that the system configuration also relies on the coding employed in the migration exercise. In this case coding can be done only once. For instance, in a configuration, an item of the system may be subject to repair. It often proves implausible to do coding at one location only because there is need to make distinctions between codes. In this regard coding is done along many lines in order to make differences on the data to be coded. Moreover, this makes it possible to prompt responses regarding the data. The software also incorporates the survey glim specifically for the linear modeling. According to Avery (2009), the other form of application of the LISREL strategy is in the 32-bit case. It helps in the structural; modeling and the multilevel modeling. The methods are applicable for the survey on the continuous variables and the incomplete sets of data. The software also incorporates the random measurement errors in the regression process. This takes care of the constant variables of analysis. References Avery, G. C. (2009). Structural Models of Wage and Employment Dynamics. London [u.a.], SAGE Bae, J. and Lawler, J. J. (2010). Structural modelling. LISREL Journal, Vol. 43, No.3: 502 – 517. Baker, T. E. (2011). Unstable Consumer Learning Models. Flushing, NY, Looseleaf Law Publications, Inc. Butler, C. (2008). Structural Equation Modeling: Foundations and Extensions. Basingstoke, Palgrave Macmillan. Cassell, G. (2009). Structural equation model. Journal of structural equation model, 34 (2), 201 – 231. Dijkstra, P. (2009). Methods of structural modelling. The structural modeling journal, 7(3), 235- 243. Guo, S. (2012). Structural equation modeling. Oxford: Oxford University Press. Samiec, E. (2009). Structural Equation Modeling: Foundations and Extensions. Mountain View, Calif, Davies-Black Pub. Delaney, J.T. (2011). The structural model. A multidisciplinary Journal, Vol.39, No.4: 949 – 969. Birkhoff, J. (2011). Structural Equation Modeling: A Second Course. Charlottesville, University of Virginia Press. Gill, R. (2010). Handbook of Structural Equation Modeling. London, SAGE Publications Ltd. Hancock, G. R. (2012). Structural equation modeling: A second course. . San Francisco, Calif, Berrett-Koehler Publishers. United States. (2005) Structural Equation Modeling. [S.l.], Wildside Pr. Hillary, D. (2010). Principles of statistics. Journal of modelling, 35(2), 211-220. Singer, P. M. (2009). Structural Equation Modeling with LISREL. Chicago, American Library Association. Whicker, M. L. (2010). A Beginners Guide to Structural Equation Modeling. Ann Arbor, Univ. of Michigan Press. Wheatley, M. J. (2007). Structural Modeling and Analysis. San Francisco, Calif, Berrett-Koehler Publishers. Perez, D. W. (2012). Term-Structure Models: A Graduate Course. Clifton Park, N.Y., Delmar, Cengage Learning. Fernando, M. (2007). Structural Models in Real Time Cheltenham, UK, Edward Elgar. Flamholtz, E. (2006). Effects of Fiscal Stimulus in Structural Models. Boston, Kluwer Academic Publishers. Northouse, P. G. (2010). Using LISREL for structural equation modeling. Thousand Oaks, Sage Publications. Mill, John, S. The subjection of Women. San Francisco Bay Area womens Journal, 2013:32- 81. Murray, L. (2007). LISREL 8: Users Reference Guide. [Ottawa]. Laurent A, (2012). A structural equation model. The journal of structural modelling, 15: 91 – 102 Saleem, S. (2010). Interactive LISREL: Users Guide. New Delhi, Pearson. Siebens, Herman. (2007). LISREL Issues, Debates and Strategies. Garant Uitgevers N V. Read More