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Course module: 201800138
201800138
Measurement and modelling with social data
Course infoSchedule
Course code201800138
ECTS Credits7.5
Category / Level3 (Bachelor Advanced)
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Social Sciences; Undergraduate School Sociale Wetenschappen; Sociologie;
Contact persondr. W. Przepiorka
E-mailW.Przepiorka@uu.nl
Lecturers
Contactperson for the course
dr. W. Przepiorka
Other courses by this lecturer
Lecturer
dr. W. Przepiorka
Other courses by this lecturer
Lecturer
dr. H.M. Weesie
Feedback and availability
Other courses by this lecturer
Teaching period
2  (11/11/2019 to 31/01/2020)
Teaching period in which the course begins
2
Time slotB: TUE-morning, THU-afternoon
Study mode
Full-time
RemarkThose who did not complete the course Practicum Data-analyse must complete a self-assessment test before signing up.
Enrolment periodfrom 03/06/2019 up to and including 30/06/2019
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Post-registrationYes
Post-registration openfrom 19/08/2019 up to and including 20/08/2019
Waiting listNo
Course placement processniet van toepassing
Aims
  • Assessing reliability and validity of empirical measurement in conventional (e.g., surveys) and new sources of social data (e.g., online process data)
  • Learning different statistical techniques for scale, factor, regression and social network analysis
  • Applying these techniques to social data from conventional and new sources to address substantial research questions
  • Getting acquainted with different types of social data and learning to cope with statistical measurement and modelling issues
Content
The course focuses on the `Empirical research' , in the `Problems-Theory-Empirical research-Policy implications'-sequence that characterizes the various steps in analytical social science.

The course introduces some of the main methods of empirical measurement and statistical modeling that are used in quantitative sociological research. These are various methods to assess the validity and reliability of measurement, such as reliability analysis and factor analysis, as well as advanced methods of multiple regression analysis. These methods are used to address substantial research questions. For example: Do strong family ties inhibit trust? How does reputation affect online traders’ business success? How are network density and interethnic diversity related?
The first week will be a recap of multiple regression and measurement basics. The rest of the course is divided in two parts, each lasting four weeks. In the first part, students will study measurement and statistical modeling issues related to trust and reputation by means of survey (e.g., General Social Survey) and online process data (e.g., transaction data from peer-to-peer online markets). In the second part, students will study measurement and statistical modeling issues related to social networks by means of survey and social network data (e.g., complete class networks).
In the first three weeks of each part, there will be weekly assignments asking students to solve problems of measurement and statistical modeling that arise in the respective data sets. For example, students will be asked to replicate an analysis described in a research paper or extend the analysis addressing a novel research question. The assignments will also give students the opportunity to get acquainted with different kinds of social data. In the fourth week of each part, students write a research essay in which they address particular research questions, conduct an explanatory analysis and explicate the social relevance of their results as well as the limitations of their analysis.
Students meet three times a week in lectures, tutorials and computer lab sessions. In the lectures, the methodological and statistical theories behind measurement and modeling issues in social data analysis are discussed. In the tutorials, the results of the assignments and research essays are discussed. In the computer lab sessions, students apply the research skills and statistical theory to real life examples and prepare their assignments and research essays.

 
Assumed knowledge
Basic statistics (descriptive and inference), basic regression analysis (simple and multiple), and basic skills of SPSS including working with syntax. Sound knowledge of English (spoken and written).

The course is designed for third year sociology students. The language of the instruction and presentations is English.

Note: this course is also part of the dedicated minor Social Sciences and Economics.
Entry requirements
The following course module must be completed:
- Practicum data-analyses (200300022)
Prerequisite knowledge
Basic statistics (descriptive and inference), basic regression analysis (simple and multiple), and basic skills of SPSS including working with syntax. Sound knowledge of English (spoken and written).

Those who did not complete the course Practicum Data-analyse are offered a self-assessment test to help them decide whether they meet the requirements with respect to knowledge and skills.
Required materials
Book
Collier, J., Using SPSS Syntax, A Beginner’s Guide. Los Angeles: Sage, 2010. ISBN is 978-1483333434 or similar SPSS manual.
Instructional formats
Access

Computer lab sessions

General remarks
In the computer lab sessions, students apply the research skills and statistical theory to real life examples and prepare for assignments and research essays.

Class session preparation
Students are expected to familiarize themselves with the data used in the computer lab session.

Contribution to group work
Students participate actively to prepare the assignments.

Lectures

General remarks
In the lectures, the methodological and statistical theories behind measurement and modeling issues in social data analysis are discussed.

Class session preparation
Students read the required literature in advance to obtain understanding of the topic of the lecture.

Tutorials

General remarks
In the tutorials, the results of the assignments and research essays are discussed and the new assignments are introduced.

Class session preparation
Students prepare reports on the assignments and research essays, and actively participate in the discussion during the tutorials.

Tests
Final result
Test weight100
Minimum grade5.5

Assessment
Essays (40%), min. grade 5.0 and assignments (60%), min. grade 5.0. See course manual for details.
Knowledge of different statistical techniques for scale, factor, regression and social network analysis and their application to social data from conventional and new sources to cope with statistical measurement and modelling issues.

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