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        1. 社會科學的方法和統計







          此專項課程涵蓋社會科研問題的研究方法、 設計和統計分析。在期末畢業項目中,您將應用所學技能,設計自己的研究課題,收集數據,進行分析,并使用統計方法匯報研究成果。

          第 1 門課程


          即將開課的班次:5月 9 — 7月 11。每隔 3 周會有一個新班次開課。
          課程學習時間 8 weeks, 4-5 hours/week

          Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research!

          This course will cover the fundamental principles of science, some history and philosophy of science, research designs, measurement, sampling and ethics. The course is comparable to a university level introductory course on quantitative research methods in the social sciences, but has a strong focus on research integrity. We will use examples from sociology, political sciences, educational sciences, communication sciences and psychology.

          第 2 門課程


          即將開課的班次:5月 9 — 7月 11。每隔 3 周會有一個新班次開課。
          課程學習時間 8 weeks of study, 4-6 hours/week

          In this course you will be introduced to the basic ideas behind the qualitative research in social science. You will learn about data collection, description, analysis and interpretation in qualitative research. Qualitative research often involves an iterative process. We will focus on the ingredients required for this process: data collection and analysis.
          You won’t learn how to use qualitative methods by just watching video’s, so we put much stress on collecting data through observation and interviewing and on analysing and interpreting the collected data in other assignments.
          Obviously, the most important concepts in qualitative research will be discussed, just as we will discuss quality criteria, good practices, ethics, writing some methods of analysis, and mixing methods.
          We hope to take away some prejudice, and enthuse many students for qualitative research.

          第 3 門課程


          即將開課的班次:5月 23 — 7月 25。每隔 3 周會有一個新班次開課。
          課程學習時間 8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week.

          Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization – the course Inferential Statistics.

          In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.

          The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work.

          The third part of the course consists of an introduction to methods of inferential statistics – methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests.

          You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.

          第 4 門課程


          當前班次:4月 25 — 6月 20。每隔 3 周會有一個新班次開課。
          課程學習時間 7 weeks of study, 1-3 hours/week

          Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.

          We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software.

          For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar’s test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).



          于 June 6, 2016 開始

          The capstone consists of a research project that you will perform in collaboration with fellow students. You will formulate a research hypothesis and design and collect data or work with a secondary data set provided by one of our external partners (TBA). You will document and analyze the data and write a research report. Depending on the topic you choose, the project may involve developing your own questionnaire and collecting your own data or contributing to a research project running at the University of Amsterdam or one of our external partners.

          Online learning to jumpstart your future.
          • Coursera
          • edX
          • OpenLearning
          • FutureLearn
          • iversity
          • Udacity
          • NovoEd
          • Canvas
          • Open2Study
          • Google
          • ewant
          • FUN
          • IOC-Athlete-MOOC
          • World-Science-U
          • Codecademy
          • CourseSites
          • opencourseworld
          • ShareCourse
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          • MiriadaX
          • JANUX
          • openhpi
          • Stanford-Open-Edx
          • 網易云課堂
          • 中國大學MOOC
          • 學堂在線
          • 頂你學堂
          • 華文慕課
          • 好大學在線CnMooc
          • 以及更多...

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