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Methods and Statistics in Social Sciences Specialization

to Conduct Solid Research in Social Science

Earn a Certificate

  • Specialization via Coursera and University of Amsterdam
  • $245 for 6 months
  • 4-8 hours a week of effort
  • 3 courses + capstone project
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Title
Methods and Statistics in Social Sciences
Rating
☆☆☆☆☆ (0 Reviews)
Overview
To conduct solid research in social science
Credential Type
Provider
Cost
$245
Effort
4-8 hours a week
Duration
6 months

You'll learn to separate sloppy science from solid science and to do your own research. This specialization is comparable to an undergraduate program in methods and statistics in any social or behavioral science. The program targets students who want to develop their research skills to become more critical consumers of research information, or want to become (better) researchers. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses.

★★★★☆ (1) 6 weeks 31st Aug, 2015
Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research!
★★★★★ (1) 6 weeks 26th Oct, 2015
Learn about the qualitative approach to the social and behavioral sciences, using qualitative methods of inquiry and analysis. Learn to evaluate qualitative research and how to collect qualitative data and perform qualitative analyses yourself.
★★★★★ (1) 6 weeks 4th Jan, 2016
Learn about descriptive statistics, and how they are used and misused in the social and behavioral sciences. Learn how to critically evaluate the use of descriptive statistics in published research and how to generate descriptive statistics yourself, using freely available statistical software.
★★★☆☆ (4) 7 weeks 26th Feb, 2018
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).
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