Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal Science
The purpose of this study was to evaluate the performance of multiple imputation method in case that missing observation structure is at random and completely at random from the approach of general linear mixed model. The application data of study was consisted of a total 77 heads of Norduz ram lamb...
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| Format: | Article |
| Language: | English |
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Turkish Science and Technology Publishing (TURSTEP)
2015-11-01
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| Series: | Turkish Journal of Agriculture: Food Science and Technology |
| Subjects: | |
| Online Access: | http://www.agrifoodscience.com/index.php/TURJAF/article/view/511 |
| _version_ | 1797907799190011904 |
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| author | Gazel Ser Cafer Tayyar Bati |
| author_facet | Gazel Ser Cafer Tayyar Bati |
| author_sort | Gazel Ser |
| collection | DOAJ |
| description | The purpose of this study was to evaluate the performance of multiple imputation method in case that missing observation structure is at random and completely at random from the approach of general linear mixed model. The application data of study was consisted of a total 77 heads of Norduz ram lambs at 7 months of age. After slaughtering, pH values measured at five different time points were determined as dependent variable. In addition, hot carcass weight, muscle glycogen level and fasting durations were included as independent variables in the model. In the dependent variable without missing observation, two missing observation structures including Missing Completely at Random (MCAR) and Missing at Random (MAR) were created by deleting the observations at certain rations (10% and 25%). After that, in data sets that have missing observation structure, complete data sets were obtained using MI (multiple imputation). The results obtained by applying general linear mixed model to the data sets that were completed using MI method were compared to the results regarding complete data. In the mixed model which was applied to the complete data and MI data sets, results whose covariance structures were the same and parameter estimations and standard estimations were rather close to the complete data are obtained. As a result, in this study, it was ensured that reliable information was obtained in mixed model in case of choosing MI as imputation method in missing observation structure and rates of both cases. |
| first_indexed | 2024-04-10T10:43:37Z |
| format | Article |
| id | doaj.art-c256524e2cde44e38a042d8c52721202 |
| institution | Directory Open Access Journal |
| issn | 2148-127X |
| language | English |
| last_indexed | 2024-04-10T10:43:37Z |
| publishDate | 2015-11-01 |
| publisher | Turkish Science and Technology Publishing (TURSTEP) |
| record_format | Article |
| series | Turkish Journal of Agriculture: Food Science and Technology |
| spelling | doaj.art-c256524e2cde44e38a042d8c527212022023-02-15T16:20:28ZengTurkish Science and Technology Publishing (TURSTEP)Turkish Journal of Agriculture: Food Science and Technology2148-127X2015-11-0131292693210.24925/turjaf.v3i12.926-932.511241Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal ScienceGazel Ser0Cafer Tayyar Bati1Yüzüncü Yıl Üniversitesi, Ziraat Fakültesi, Zootekni Bölümü, Biyometri ve Genetik Ana Bilim Dalı, 65080 VanYüzüncü Yıl Üniversitesi, Ziraat Fakültesi, Zootekni Bölümü, Biyometri ve Genetik Ana Bilim Dalı, 65080 VanThe purpose of this study was to evaluate the performance of multiple imputation method in case that missing observation structure is at random and completely at random from the approach of general linear mixed model. The application data of study was consisted of a total 77 heads of Norduz ram lambs at 7 months of age. After slaughtering, pH values measured at five different time points were determined as dependent variable. In addition, hot carcass weight, muscle glycogen level and fasting durations were included as independent variables in the model. In the dependent variable without missing observation, two missing observation structures including Missing Completely at Random (MCAR) and Missing at Random (MAR) were created by deleting the observations at certain rations (10% and 25%). After that, in data sets that have missing observation structure, complete data sets were obtained using MI (multiple imputation). The results obtained by applying general linear mixed model to the data sets that were completed using MI method were compared to the results regarding complete data. In the mixed model which was applied to the complete data and MI data sets, results whose covariance structures were the same and parameter estimations and standard estimations were rather close to the complete data are obtained. As a result, in this study, it was ensured that reliable information was obtained in mixed model in case of choosing MI as imputation method in missing observation structure and rates of both cases.http://www.agrifoodscience.com/index.php/TURJAF/article/view/511Eksik gözlem yapılarıçoklu atamatekrarlı verikarışık model |
| spellingShingle | Gazel Ser Cafer Tayyar Bati Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal Science Turkish Journal of Agriculture: Food Science and Technology Eksik gözlem yapıları çoklu atama tekrarlı veri karışık model |
| title | Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal Science |
| title_full | Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal Science |
| title_fullStr | Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal Science |
| title_full_unstemmed | Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal Science |
| title_short | Evaluation of Multiple Imputation in Missing Data Analysis: An Application on Repeated Measurement Data in Animal Science |
| title_sort | evaluation of multiple imputation in missing data analysis an application on repeated measurement data in animal science |
| topic | Eksik gözlem yapıları çoklu atama tekrarlı veri karışık model |
| url | http://www.agrifoodscience.com/index.php/TURJAF/article/view/511 |
| work_keys_str_mv | AT gazelser evaluationofmultipleimputationinmissingdataanalysisanapplicationonrepeatedmeasurementdatainanimalscience AT cafertayyarbati evaluationofmultipleimputationinmissingdataanalysisanapplicationonrepeatedmeasurementdatainanimalscience |