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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
<p>Can we still put our trust in the social and behavioural sciences? Cases of social scientists exposed as frauds keep turning up and many disciplines are under fire for their failure to replicate key results. No wonder the integrity of our field is being questioned; sloppy science is starting to seem the norm rather than the exception!</p> <p>As social scientist <a href="http://www.nature.com/polopoly_fs/7.6716.1349271308!/suppinfoFile/Kahneman%20Letter.pdf" target="_blank">Daniel Kahneman suggests</a>, it is time for the social sciences to clean house. We will try to answer his call with a series of courses that explain the scientific principles of research and how methodology and statistics can help to ensure that research is solid. We will explain the basics and put them into context by showing you how things can go horribly wrong when methods and statistics are abused. And we will teach you how to recognize these questionable research practices - after the fact - in published articles.</p> <p>This first course, Solid Science: Research Methods (in the Social and Behavioral Sciences), will cover the fundamental principles of science, some history and philosophy of science, research designs, measurement, sampling and ethics. This basic material will lay the groundwork for the more technical stuff in subsequent courses. 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.</p> <p>Please note that this course will focus on quantitative methods, qualitative methods will be treated in a separate course.</p>
★★★★★ (1) 6 weeks 26th Oct, 2015
In this course you will be introduced to the origin and philosophies behind the qualitative approach to empirical science. You will learn about data collection, description, analysis and interpretation in qualitative research. The qualitative approach often involves an iterative process. We will focus on the basic ingredients required for this process: data collection and analysis. A good qualitative analysis consists of a general strategy of analysis, clear analytic 'actions' and documentation of all the steps taken. An important analytical action is coding parts of the material. This forms the basis for the categorisation and interpretation of the data. In this course you will learn to use statistical software to perform the qualitative analysis. We will also discuss and compare different types of analysis and interpretation. The most important concepts in qualitative analysis will be discussed in light of these different types.<br>
★★★★★ (1) 6 weeks 4th Jan, 2016
Understanding statistics is essential to understand research in the social and behavioral sciences. In almost all research studies, statistics are necessary to decide whether the results support the research hypothesis. In this course you will learn the basics of descriptive statistics; not just how to calculate them, but also how to evaluate them. An important part of the material treated in this course will prepare you for the next course in the specialization, namely the course Inferential Statistics.&nbsp;<br><br>We will start with the concepts variable and data, the difference between population and sample and types of data. Then we will consider the most important measures for centrality (mean, median and mode) and spread (standard deviation and variance). These will be followed by the concepts contingency, correlation and regression. All these statistics make it possible to represent large amounts of data in a clear way, enabling us to spot interesting patterns.&nbsp;<br><br>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. We will end the course with a short preview of inferential statistics - statistics that help us decide whether the differences between groups or correlations between variables that we see in our data are strong enough to conclude that our predictions were confirmed and our hypothesis is supported.<br><br>You will not only learn about all these concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.
★★★☆☆ (4) 7 weeks 8th Apr, 2019
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.<br /> <br /> 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. <br /> <br /> 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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