Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research!
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阿姆斯特丹大学
A modern university with a rich history, the University of Amsterdam
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Before we get started...
In this first module we'll consider the basic principles of the scientific method, its history and its philosophies. But before we start talking methods, I'll give you a broad sense of what the course is about and how it's organized. Are you new to Coursera or still deciding whether this is the course for you? Then make sure to check out the 'Introduction' and 'What to expect' section below, so you'll have the essential information you need to decide and to do well in this course! If you have any questions about the course format, deadlines or grading, you'll probably find the answers here. Are you a Coursera veteran and anxious to get started? Then you might want to skip ahead to the first course topic: the Origins of the Scientific Method. You can always check the general information later. Veterans and newbies alike: Don't forget to introduce yourself in the 'meet and greet' forum!
Origins of the scientific method
Science is all about gaining knowledge, coming up with the best possible explanations of the world around us. So how do we decide which explanation is the best one? How do we make sure our explanations are accurate?
The Scientific Method
In the first module we discussed how the scientific method developed, general philosophical approaches and the types of knowledge science aims to find. In this second module we'll make these abstract principles and concepts a little more concrete by discussing the empirical cycle and causality in more detail. We’ll see how, and in what order these concepts are implemented when we conduct a research study. We'll also consider the main criteria for evaluating the methodological quality of a research study: Validity and reliability. The focus will be on internal validity and how internal validity can be threatened.
Research Designs
In the previous module we discussed the empirical cycle, causality and the criteria for methodological quality, focusing on threats to internal validity. In this module we'll consider the most frequently used research designs and we'll see how they address threats to internal validity. We'll look at experimental, quasi-experimental and correlational designs, as well as some other designs you should be familiar with. To understand and appreciate these designs we will discuss some general concepts such as randomization and matching in a little more detail.
Measurement
Choosing a design is only the first step in the deduction phase (remember the empirical cycle?). The second step is deciding on specific ways to measure the variables of interest and disinterest.
审阅
- 5 stars80.92%
- 4 stars14.94%
- 3 stars2.44%
- 2 stars0.71%
- 1 star0.95%
来自计量方法的热门评论
This is a really good course, comprehensive and useful to anyone wanting to know about quant methods. It's pretty tough and you need to study, but if you finish it you'll definitely learn a lot.
This is a great starting course on the topic of Quant Methods. The program could take an entire semester or two if you want to go in depth, but as a starter, it was just right.
This course is well taught, easy to follow and very interesting. I recommend this course for anyone who wants to gain a better understanding of research methods and critically reading research.
Excellent course! Well narrated, even entertaining, VERY well structured, with supporting material, and a great mix of theory and examples or discussions. Perhaps the best MOOC I've ever encountered.
关于 社会科学的方法和统计 专项课程
Identify interesting questions, analyze data sets, and correctly interpret results to make solid, evidence-based decisions.

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