| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Biostatistics | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical |  |
| 1st year Master Biostatistics - icp | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Quantitative Epidemiology | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Master Quantitative Epidemiology - icp | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. | | | - DC
| The student is an effective oral communicator in their own field. | | | - DC
| The student is an effective oral communicator, both within their own field as well as across disciplines. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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Key terms/content : survey sampling, survey design, survey analysis, stratification, multi stage sampling, clustering, weighting.
Learning outcomes
The student is familiar with survey design and analysis, against the context of other study designs. The focus is on general understanding and application, rather than on mathematical rigour. The student can design a survey and analyse survey data. The student can select and apply appropriate software tools to this effect.
Aims
Commonly used sample survey designs are studied: random, systematic, multi-stage, and clustered sampling. Their statistical and practical advantages and disadvantages are discussed. The implications for statistical analysis are carefully assessed. Other essential topics include: type of survey (face-to-face, mail, telephone), questionnaire design, and non-response.
The student can recognize and distinguish between the main survey designs and analysis methods. The student can apply these methodes in a realistic setting. The student has a general understanding of the concepts underlaying the various methods. This course contributes mainly to the objectives and skills:
Topic-specific competences in own discipline (VEE): the student should :
1.have insight in the design of studies and the consequences of the design on further analysis (experimental studies, observational studies, survey methodology ... );
2.know the limitations of statistical analyses, take them into account and communicate them;
3.know the professional literature and be able to efficiently collect and evaluate statistical information (journals, book references, internet ...) .
Cross-disciplinary competences (DCE): the student should :
1.be able to report clearly and professionally, also in an international context, and understand the criteria and guidelines for scientific reports ;
2.be able to plan and finish individual tasks as well as group assignments on time ;
3.develop a feeling on how to achieve effective consulting. This includes adjusting to the terminology of the client, clarifying and reformulating scientific questions into understandable statistical procedures, making arrangements on the procedure to follow and formulating clear conclusions
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Distance learning ✔
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Lecture ✔
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Project ✔
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Response lecture ✔
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Period 2 Credits 5,00
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | All components of the evaluation have to be taken up. |
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| Consequences | Students who do not participate in one or more of the evaluation components will receive an "X" score. |
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| Additional information | For the oral part of the exam, the range is [-2;10]. This ensures that students cannot merely pass on the written part, not even if they obtain the maximum score on the written part. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The assignment for the project and format of the oral exam is identical. |
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| Recommended course material |
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The course slides set out with a list of texts, primarily books, that are useful background reading. They are not compulsory. They can be relevant, in particular towards future use of sampling theory concepts. |
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| 1st year Master of Science in Transportation Sciences | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
| 2nd year Master of Transportation Sciences | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences. He/she is able to apply the concepts, methods and (research) techniques in the field of transportation sciences adequately and autonomously. | - EC
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences and is able to apply them adequately and autonomously. | - EC
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences. He/she is able to apply the concepts, methods and (research) techniques in the field of transportation sciences adequately and autonomously. | - EC
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences and is able to apply them adequately and autonomously. | - EC
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences. He/she is able to apply the concepts, methods and (research) techniques in the field of transportation sciences adequately and autonomously. | - EC
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences and is able to apply them adequately and autonomously. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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Key terms/content : survey sampling, survey design, survey analysis, stratification, multi stage sampling, clustering, weighting.
Learning outcomes
The student is familiar with survey design and analysis, against the context of other study designs. The focus is on general understanding and application, rather than on mathematical rigour. The student can design a survey and analyse survey data. The student can select and apply appropriate software tools to this effect.
Aims
Commonly used sample survey designs are studied: random, systematic, multi-stage, and clustered sampling. Their statistical and practical advantages and disadvantages are discussed. The implications for statistical analysis are carefully assessed. Other essential topics include: type of survey (face-to-face, mail, telephone), questionnaire design, and non-response.
The student can recognize and distinguish between the main survey designs and analysis methods. The student can apply these methodes in a realistic setting. The student has a general understanding of the concepts underlaying the various methods. This course contributes mainly to the objectives and skills:
Topic-specific competences in own discipline (VEE): the student should :
1.have insight in the design of studies and the consequences of the design on further analysis (experimental studies, observational studies, survey methodology ... );
2.know the limitations of statistical analyses, take them into account and communicate them;
3.know the professional literature and be able to efficiently collect and evaluate statistical information (journals, book references, internet ...) .
Cross-disciplinary competences (DCE): the student should :
1.be able to report clearly and professionally, also in an international context, and understand the criteria and guidelines for scientific reports ;
2.be able to plan and finish individual tasks as well as group assignments on time ;
3.develop a feeling on how to achieve effective consulting. This includes adjusting to the terminology of the client, clarifying and reformulating scientific questions into understandable statistical procedures, making arrangements on the procedure to follow and formulating clear conclusions
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Distance learning ✔
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Lecture ✔
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Project ✔
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Response lecture ✔
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Period 2 Credits 6,00
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | All components of the evaluation have to be taken up. |
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|
| Consequences | Students who do not participate in one or more of the evaluation components will receive an "X" score. |
|
|
|
| Additional information | For the oral part of the exam, the range is [-2;10]. This ensures that students cannot merely pass on the written part, not even if they obtain the maximum score on the written part. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The assignment for the project and format of the oral exam is identical. |
|
|
|
|
|
| Recommended course material |
| |
The course slides set out with a list of texts, primarily books, that are useful background reading. They are not compulsory. They can be relevant, in particular towards future use of sampling theory concepts. |
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|
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| Exchange Programme Statistics | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
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| |
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|
Key terms/content : survey sampling, survey design, survey analysis, stratification, multi stage sampling, clustering, weighting.
Learning outcomes
The student is familiar with survey design and analysis, against the context of other study designs. The focus is on general understanding and application, rather than on mathematical rigour. The student can design a survey and analyse survey data. The student can select and apply appropriate software tools to this effect.
Aims
Commonly used sample survey designs are studied: random, systematic, multi-stage, and clustered sampling. Their statistical and practical advantages and disadvantages are discussed. The implications for statistical analysis are carefully assessed. Other essential topics include: type of survey (face-to-face, mail, telephone), questionnaire design, and non-response.
The student can recognize and distinguish between the main survey designs and analysis methods. The student can apply these methodes in a realistic setting. The student has a general understanding of the concepts underlaying the various methods. This course contributes mainly to the objectives and skills:
Topic-specific competences in own discipline (VEE): the student should :
1.have insight in the design of studies and the consequences of the design on further analysis (experimental studies, observational studies, survey methodology ... );
2.know the limitations of statistical analyses, take them into account and communicate them;
3.know the professional literature and be able to efficiently collect and evaluate statistical information (journals, book references, internet ...) .
Cross-disciplinary competences (DCE): the student should :
1.be able to report clearly and professionally, also in an international context, and understand the criteria and guidelines for scientific reports ;
2.be able to plan and finish individual tasks as well as group assignments on time ;
3.develop a feeling on how to achieve effective consulting. This includes adjusting to the terminology of the client, clarifying and reformulating scientific questions into understandable statistical procedures, making arrangements on the procedure to follow and formulating clear conclusions
|
|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Response lecture ✔
|
|
|
|
Period 2 Credits 5,00
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | All components of the evaluation have to be taken up. |
|
|
|
| Consequences | Students who do not participate in one or more of the evaluation components will receive an "X" score. |
|
|
|
| Additional information | For the oral part of the exam, the range is [-2;10]. This ensures that students cannot merely pass on the written part, not even if they obtain the maximum score on the written part. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The assignment for the project and format of the oral exam is identical. |
|
|
|
|
|
| Recommended course material |
| |
The course slides set out with a list of texts, primarily books, that are useful background reading. They are not compulsory. They can be relevant, in particular towards future use of sampling theory concepts. |
|
|
|
|
|
| 1st year Master Bioinformatics | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Master Data Science | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. | | | - DC
| The student is an effective oral communicator in their own field. | | | - DC
| The student is an effective oral communicator, both within their own field as well as across disciplines. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
Key terms/content : survey sampling, survey design, survey analysis, stratification, multi stage sampling, clustering, weighting.
Learning outcomes
The student is familiar with survey design and analysis, against the context of other study designs. The focus is on general understanding and application, rather than on mathematical rigour. The student can design a survey and analyse survey data. The student can select and apply appropriate software tools to this effect.
Aims
Commonly used sample survey designs are studied: random, systematic, multi-stage, and clustered sampling. Their statistical and practical advantages and disadvantages are discussed. The implications for statistical analysis are carefully assessed. Other essential topics include: type of survey (face-to-face, mail, telephone), questionnaire design, and non-response.
The student can recognize and distinguish between the main survey designs and analysis methods. The student can apply these methodes in a realistic setting. The student has a general understanding of the concepts underlaying the various methods. This course contributes mainly to the objectives and skills:
Topic-specific competences in own discipline (VEE): the student should :
1.have insight in the design of studies and the consequences of the design on further analysis (experimental studies, observational studies, survey methodology ... );
2.know the limitations of statistical analyses, take them into account and communicate them;
3.know the professional literature and be able to efficiently collect and evaluate statistical information (journals, book references, internet ...) .
Cross-disciplinary competences (DCE): the student should :
1.be able to report clearly and professionally, also in an international context, and understand the criteria and guidelines for scientific reports ;
2.be able to plan and finish individual tasks as well as group assignments on time ;
3.develop a feeling on how to achieve effective consulting. This includes adjusting to the terminology of the client, clarifying and reformulating scientific questions into understandable statistical procedures, making arrangements on the procedure to follow and formulating clear conclusions
|
|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Response lecture ✔
|
|
|
|
Period 2 Credits 5,00
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | All components of the evaluation have to be taken up. |
|
|
|
| Consequences | Students who do not participate in one or more of the evaluation components will receive an "X" score. |
|
|
|
| Additional information | For the oral part of the exam, the range is [-2;10]. This ensures that students cannot merely pass on the written part, not even if they obtain the maximum score on the written part. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The assignment for the project and format of the oral exam is identical. |
|
|
|
|
|
| Recommended course material |
| |
The course slides set out with a list of texts, primarily books, that are useful background reading. They are not compulsory. They can be relevant, in particular towards future use of sampling theory concepts. |
|
|
|
|
|
1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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