Logistische Regressionsanalyse

Behnke, Joachim

doi:10.1007/978-3-658-05082-5

Cited by 12 publications

(6 citation statements)

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“…To find out whether the association between the formal contract and ECRs' intention to leave is mediated by integration in the institution and the scientific community, overall job satisfaction, and control variables, hierarchical binary logistic regression analyses are conducted in five consecutive models. To be able to compare the models, the average marginal effects are shown in Table 5 (Behnke 2015;Mood 2009). The question in focus is how the correlates of formal contract change with the stepwise integration of further factors (Baron/Kenny 1986).…”

Section: Regression Modelsmentioning

confidence: 99%

Fixed-term employment and leaving intention. An analysis of junior academics across Europe

Höhle

2024

Career Paths Inside and Outside Academia

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The academic career systems in Europe differ significantly. While in tenure systems, permanent positions can be obtained shortly after the doctorate, in up-or-out systems, most researchers remain in fixed-term employment until they become professors. Therefore, the article focuses on how the type of contract affects the intention of post-doctoral researchers to leave academia in different countries, using theoretical labor market concepts as well as the social-cognitive approach. Findings based on EUROAC data from ten European countries show that more researchers in up-or-down systems intend to leave academia than in tenure systems. This applies to both temporary and permanent researchers. Still, the duration of work contract -especially temporary employment without prospects of permanent employment -is a significant predictor for leaving academia even after controlling for other factors. In contrast, job satisfaction plays an important role in both groups for the remain. In addition, the number of publications only has a significant influence in tenure systems and does not play a role in the up-or-out systems. It is also only in tenure systems that women with children show a lower leaving intention -whereas in Germany for example, the compatibility of an academic career with a family is discussed as a problem area.

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Section: Regression Modelsmentioning

confidence: 99%

Fixed-term employment and leaving intention. An analysis of junior academics across Europe

Höhle

2024

Career Paths Inside and Outside Academia

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“…However, the dichotomous dependent variable has the disadvantage that potential cost savings can not directly be assigned [9]. In addition to regression models, classification models such as Support Vector Machine (SVM) and Decision Tree (DT) methods can be applied [16,17,18]. Classification is the assignment of data objects to a suitable class, whereby, for example, the minimization of the classification error or the maximization of the degree of affiliation are used as performance evaluation criteria [19].…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…Drosou and Koukouvinos [16] use SVM to find an optimal hyperplane that separates cost-intensive from "regular" patients. However, comparing different classification and predictive models, Moturu, Johnson, and Liu [17] show that SVM have the lowest performance. In their study, Bertsimas et al [18] utilize DT to classify high-cost patients.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…There are a variety of different influencing factors in literature that increase the likelihood of becoming a costintensive patient. Especially demographic variables are often used as the first factor in predictive analysis, where aspects such as age and gender are known to be reliable predictors [17,3]. Bertakis and Azari [14] intensively examine the influence of gender in their study and confirm that women are associated with higher costs.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…Although pure predictive demographic models perform worse in terms of prognosis quality compared to models with clinical variables, they provide meaningful predictions for the small amount of information available. This allows for categorization at a time when no other information is given [17]. Other important indicators are clinical variables based on the ICD9 and ICD-10 diagnostic codes [3].…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

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Real-time Prediction of the Risk of Hospital Readmissions

Wickramasinghe¹,

Degano²,

McConchie³

2019

Humanizing Technology for a Sustainable Society

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This study aims to identify predictors for patients likely to be readmitted to a hospital within 28 days of discharge and to develop and validate a prediction model for identifying patients at a high risk of readmission. Numerous attempts have been made to build similar predictive models. However, the majority of existing models suffer from at least one of the following shortcomings: the model is not based on Australian Health Data; the model uses insurance claim data, which would not be available in a real-time clinical setting; the model does not consider socio-demographic determinants of health, which have been demonstrated to be predictive of readmission risk; or the model is limited to a particular medical condition and is thus limited in scope. To address these shortcomings, we built several models to predict all-cause 28-day readmission risk and included Socioeconomic Indexes for Areas (SEIFA) data as proxies for socio-demographic determinants of health. Additionally, instead of using insurance claims data, which could require several weeks to process, we built our models using data that is readily available during the inpatient stay or at the time of discharge. The set of default prediction models that were examined include logistic regression, elastic net, random forest and adaptive boosting (Ada Boost). This study examined A not for profit tertiary healthcare organisation from fiscal year 2012-2013 through fiscal year 2017-2018. The out-of-sample results show that all of the models performed similarly and adequately to predict readmission risk.

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Enabling Robust Localization for Automated Guided Carts in Dynamic Environments

Hansen

Fuerstenberg

2017

Advanced Microsystems for Automotive Applications 2017

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Logistische Regressionsanalyse

Cited by 12 publications

References 0 publications

Fixed-term employment and leaving intention. An analysis of junior academics across Europe

Fixed-term employment and leaving intention. An analysis of junior academics across Europe

Real-time Prediction of the Risk of Hospital Readmissions

Enabling Robust Localization for Automated Guided Carts in Dynamic Environments

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