Employment contract types are often distinguished in permanent, flexible, and other types of contracts. Accurate estimates of their frequencies are valuable for socio-economic research and...Show moreEmployment contract types are often distinguished in permanent, flexible, and other types of contracts. Accurate estimates of their frequencies are valuable for socio-economic research and legislative purposes. In member states of the European Union, the Labour Force Survey (LFS) is used to acquire such estimates. In the Netherlands, in addition to the LFS, the Employment Register (ER) is available with which employment contract type frequencies can be estimated. Estimates based on the two indicators are known to differ substantially and consistently. Studies have found several plausible contributing factors for the inconsistencies. However, when taken into account (excluding measurement error), a substantive part of the inconsistencies remains unexplained. The true employment contract type can be regarded as a latent variable of which the ER and the LFS are indicators. Potentially, the inconsistencies between the indicators result from a difference in measured concept. Aside from the true employment contract type, direct effects (DEs) may exist from external covariates on the recorded employment contract type in the ER or the LFS. If so, such covariates are a source of differential item functioning (DIF) for the indicators. This study focuses on potential DIF for the ER and the LFS. An attempt is made to deduce DIF using latent class (LC) analysis. LC models in which various types of DEs are included are compared with a stepwise likelihood-ratio test (LRT) method, based on Masyn (2017), and an exhaustive Bayesian information criterion (BIC) method. Over multiple datasets, the results for both methods were inconsistent. Additionally, there was little agreement between the methods. The exhaustive BIC method was more conservative as all best-fitting models were nested in the best-fitting models of the stepwise LRT method. For testing the performance of the assessed methods in scenarios with two indicators, an additional simulation study is included. It was found that when no DEs were present, both methods deduced the correct relationships in all cases. However, when DEs were present, both methods performed poorly in deducing the correct relationships. Correct relationships between covariates and indicators were more often found when DIF was relatively simple and effect sizes were relatively large. The moderate success of the stepwise LRT method with two indicators had not been described in any literature thus far. As the results for the real data were inconsistent and the simulation study showed poor performance overall for the assessed methods, no decisive evidence was found that a specific covariate is a source of DIF for the employment contract type as recorded in the ER or the LFS. However, as there are hints for DIF, a difference in measured concept cannot be ruled out. Follow-up research should consider other avenues to investigate the question at hand as the assessed methods gave unsatisfactory results.Show less
Despite the growing popularity, no clear general definition of data science and artificial intelligence has been established. People are often left into the unknown when it comes to the specific...Show moreDespite the growing popularity, no clear general definition of data science and artificial intelligence has been established. People are often left into the unknown when it comes to the specific definition of these fields. In this study, the first step towards defining these fields is made. Three text analyses models were used to extract the general topics from various data science or artificial intelligence related program or course descriptions. These topics were used to be able to get a grasp on what skill sets are taught to data science and artificial intelligence students. Afterwards, an analysis of posterior classification of the topics per university was performed to explore the differences and similarities between the universities on their orientation of data science and artificial intelligence programs. General and specific skill sets are uncovered and differences between the universities are described in this paper. The results of this paper might be insightful for institutes that have no clear view whether their vacancies might be fit for data science or artificial intelligence graduates.Show less
The current thesis investigated the robustness of the two-step latent class analysis approach including a distal outcome variable when compared to the robustness of the traditional one-step...Show moreThe current thesis investigated the robustness of the two-step latent class analysis approach including a distal outcome variable when compared to the robustness of the traditional one-step approach and the BCH approach. The robustness was tested in two simulation studies. The first study violated the assumption of a normally distributed distal outcome variable, while the second simulation study violated the assumption of equal variance. The used model included ten indicator variables and one distal outcome. Furthermore, the model included two latent classes. We measured the performance of the three approaches with the bias and coverage rates of the estimated parameters, as well as the RMSE and the SE of the parameters, divided by the SD of the parameter. The results showed that all the three approaches performed approximately equally well. There was no severe under- or over-coverage, with the lowest averaged coverage-rate being .93. A possible explanation for these positive results was that the model used was relatively easy to estimate. Future research should therefore focus on implementing more complex models, when trying to execute similar tests of robustness.Show less
