Handling missing data in medical questionnaires : a comparative study
Missing Data plagues almost all researchers’ surveys or designed experiments. No matter how carefully they try to design their surveys to have their questions to be fully responded, , Missing data can still occur due to questions being unanswered or technical fault in the system. The problem lies wi...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2012
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/49602 |
| _version_ | 1811691971407773696 |
|---|---|
| author | Woon, Eric Sing Yong. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Woon, Eric Sing Yong. |
| author_sort | Woon, Eric Sing Yong. |
| collection | NTU |
| description | Missing Data plagues almost all researchers’ surveys or designed experiments. No matter how carefully they try to design their surveys to have their questions to be fully responded, , Missing data can still occur due to questions being unanswered or technical fault in the system. The problem lies with dealing with missing data, once it has been deemed impossible to recover the actual missing values. Traditional approaches used by researchers to handle missing data include case deletion and mean imputation. These methods are fast and easy to be implement however they do not preserve the relationships among the different variables, thus inflating the correlation. This report will look and compare different methods to overcome the problem of missing data. |
| first_indexed | 2024-10-01T06:28:22Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/49602 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:28:22Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/496022023-07-07T17:28:02Z Handling missing data in medical questionnaires : a comparative study Woon, Eric Sing Yong. School of Electrical and Electronic Engineering Centre for Modelling and Control of Complex Systems Justin Dauwels DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Missing Data plagues almost all researchers’ surveys or designed experiments. No matter how carefully they try to design their surveys to have their questions to be fully responded, , Missing data can still occur due to questions being unanswered or technical fault in the system. The problem lies with dealing with missing data, once it has been deemed impossible to recover the actual missing values. Traditional approaches used by researchers to handle missing data include case deletion and mean imputation. These methods are fast and easy to be implement however they do not preserve the relationships among the different variables, thus inflating the correlation. This report will look and compare different methods to overcome the problem of missing data. Bachelor of Engineering 2012-05-22T04:37:32Z 2012-05-22T04:37:32Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/49602 en Nanyang Technological University 63 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Woon, Eric Sing Yong. Handling missing data in medical questionnaires : a comparative study |
| title | Handling missing data in medical questionnaires : a comparative study |
| title_full | Handling missing data in medical questionnaires : a comparative study |
| title_fullStr | Handling missing data in medical questionnaires : a comparative study |
| title_full_unstemmed | Handling missing data in medical questionnaires : a comparative study |
| title_short | Handling missing data in medical questionnaires : a comparative study |
| title_sort | handling missing data in medical questionnaires a comparative study |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
| url | http://hdl.handle.net/10356/49602 |
| work_keys_str_mv | AT woonericsingyong handlingmissingdatainmedicalquestionnairesacomparativestudy |