General Information
| Course Code | S_QNRM |
|---|---|
| Credits | 6 EC |
| Period | P1 |
| Course Level | 200 |
| Language of Tuition | Dutch / English |
| Faculty | Faculty of Social Sciences |
| Course Coordinator | dr. F.A. Nagel |
| Examiner | dr. F.A. Nagel |
| Teaching Staff |
G.M. Quaedvlieg dr. F.A. Nagel |
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Part of programme(s)
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Study Group, Practical, Written partial exam, Lecture |
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Target audiences
This course is also available as:
Course Objective
At the end of the course, the student is able to:• identify advantages and disadvantages of experimental and
observational designs in terms of in-ternal and external validity
• causal models: recognize models with confounding variables, mediation
and moderation in a question or hypothe-sis
• constructing a scale and as part of scale construction apply
reliability analysis in SPSS, interpret the results and report on them
• apply analysis of variance and linear regression analysis in SPSS;
interpret the results and report on them
• interpret results in terms of underlying causal models: confounding
variables, mediation and moderation
Course Content
In this course, students learn the basics of multivariate statisticaltechniques commonly used in quantitative research in communication
science. The emphasis is on testing hypotheses about cause-and-effect
relationships and the interpretation of the analysis results, both in
experimental and in non-experimental research. This knowledge is useful
not only for properly testing hypotheses yourself, but also for
critically evaluating research conducted by others.
This course teaches the theory and practice of quantitative research
methods frequently used within communication science. The student learns
the logic and purpose of experimental and observational research
designs, as well as the advantages and disadvantages in terms of
internal and external validity. Within the context of causal modelling,
models with confounding variables, mediation and moderation will be
discussed and results of analyses will be related to these causal
models. Reliability analysis and some basic features of factor analysis
are applied in order to learn how to construct a reliable scale.
Building upon the first year’s course Descriptive and Inferential
Statistics, multivariate data analysis techniques will be introduced,
focusing on analysis of variance and linear regression analysis. All
mentioned techniques are applied using SPSS.
Additional Information Teaching Methods
lectures, working groups and SPSS labsMethod of Assessment
interim tests and group assignmentsLiterature
Field, Andy (2018) Discovering Statistics using IBM SPSS Statistics.Fifth edition. Los Angeles, London, New Delhi, Singapore, Washington DC:
Sage