Testing Procedure for Item Response Probabilities of 2Class Latent Model
This paper presents Hotelling T2 as a procedure for the testing of significance difference between the item response probabilities (ωij′s) of classes in a Latent Class Model (LCM). Parametric bootstrap technique is used in order to generate samples for ωij′s. These samples are based on the estimated...
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| Format: | Article |
| Language: | English |
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Mehran University of Engineering and Technology
2020-07-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/1708 |
| _version_ | 1818149584168288256 |
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| author | Bushra Shamshad Junaid Sagheer Siddiqui |
| author_facet | Bushra Shamshad Junaid Sagheer Siddiqui |
| author_sort | Bushra Shamshad |
| collection | DOAJ |
| description | This paper presents Hotelling T2 as a procedure for the testing of significance difference between the item response probabilities (ωij′s) of classes in a Latent Class Model (LCM). Parametric bootstrap technique is used in order to generate samples for ωij′s. These samples are based on the estimated parameters of 2-class latent model. The estimation of parameters in either situation is done using the Expectation Maximization (EM) algorithm through Maximum likelihood method. The hypothesis under consideration is whether the response probabilities (ωij′s) are equal against each item in both the classes. { H0 : ωi1 = ωi2. against H1 : =ωi1 ≠ ωi2}. If the test exhibits significant difference between response probabilities in both classes, it will be a clear indication of a presence of latent variable. We consider both training and testing data sets to develop the test. In order to apply Hotelling T2 test the basic assumptions of normality and homogeneity of variance are also checked. Chi-square goodness of fit test is used for assessing normal distribution to be good fitted on the hypothesized (bootstrap samples) based on 2-class latent model parameters for each data and Bartlett test to check heterogeneity of variances in ωij′s. Moreover, our procedure produces a minimum standard error of estimates as compared to those obtained through the package in R.Gui environment |
| first_indexed | 2024-12-11T13:09:21Z |
| format | Article |
| id | doaj.art-27a8b933c930488b90a44d70abdbb34f |
| institution | Directory Open Access Journal |
| issn | 0254-7821 2413-7219 |
| language | English |
| last_indexed | 2024-12-11T13:09:21Z |
| publishDate | 2020-07-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj.art-27a8b933c930488b90a44d70abdbb34f2022-12-22T01:06:13ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192020-07-0139365766710.22581/muet1982.2003.201708Testing Procedure for Item Response Probabilities of 2Class Latent ModelBushra Shamshad0Junaid Sagheer Siddiqui1Department of Statistics, University of Karachi, Karachi, Pakistan.Department of Statistics, University of Karachi, Karachi, Pakistan .This paper presents Hotelling T2 as a procedure for the testing of significance difference between the item response probabilities (ωij′s) of classes in a Latent Class Model (LCM). Parametric bootstrap technique is used in order to generate samples for ωij′s. These samples are based on the estimated parameters of 2-class latent model. The estimation of parameters in either situation is done using the Expectation Maximization (EM) algorithm through Maximum likelihood method. The hypothesis under consideration is whether the response probabilities (ωij′s) are equal against each item in both the classes. { H0 : ωi1 = ωi2. against H1 : =ωi1 ≠ ωi2}. If the test exhibits significant difference between response probabilities in both classes, it will be a clear indication of a presence of latent variable. We consider both training and testing data sets to develop the test. In order to apply Hotelling T2 test the basic assumptions of normality and homogeneity of variance are also checked. Chi-square goodness of fit test is used for assessing normal distribution to be good fitted on the hypothesized (bootstrap samples) based on 2-class latent model parameters for each data and Bartlett test to check heterogeneity of variances in ωij′s. Moreover, our procedure produces a minimum standard error of estimates as compared to those obtained through the package in R.Gui environmenthttps://publications.muet.edu.pk/index.php/muetrj/article/view/1708 |
| spellingShingle | Bushra Shamshad Junaid Sagheer Siddiqui Testing Procedure for Item Response Probabilities of 2Class Latent Model Mehran University Research Journal of Engineering and Technology |
| title | Testing Procedure for Item Response Probabilities of 2Class Latent Model |
| title_full | Testing Procedure for Item Response Probabilities of 2Class Latent Model |
| title_fullStr | Testing Procedure for Item Response Probabilities of 2Class Latent Model |
| title_full_unstemmed | Testing Procedure for Item Response Probabilities of 2Class Latent Model |
| title_short | Testing Procedure for Item Response Probabilities of 2Class Latent Model |
| title_sort | testing procedure for item response probabilities of 2class latent model |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/1708 |
| work_keys_str_mv | AT bushrashamshad testingprocedureforitemresponseprobabilitiesof2classlatentmodel AT junaidsagheersiddiqui testingprocedureforitemresponseprobabilitiesof2classlatentmodel |