| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Shafiee Kamalabad, Mahdi | |
| dc.contributor.author | Wensch, Jesse van der | |
| dc.date.accessioned | 2023-07-25T00:02:14Z | |
| dc.date.available | 2023-07-25T00:02:14Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/44309 | |
| dc.description.abstract | [""Missing data can have significant effects on reliability of results and lead to incorrect
conclusions. This study examines the effectiveness of multiple imputation in relational event
history data. The study compares the estimates of a relational event model of the complete data
with a 100 simulations where missing data was generated using the assumption that the data is
missing completely at random (MCAR). It was found that, overall, the imputation method gave
accurate estimates. However, the significance of the estimations changed from being not
significant to significant. This change in significance should be taken into consideration when
interpreting results after imputation."] | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | Exploring whether multiple imputation is a viable method for imputing missing data for relational event history data. | |
| dc.title | Imputing Missing values in Relational Event History data: A Framework for Social Network Research | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | Relational event history; REH; relational event model; REM, missing values;
multiple imputation | |
| dc.subject.courseuu | Applied Data Science | |
| dc.thesis.id | 20039 | |
