About this course: 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.
Created by: University of Amsterdam
Taught by: Matthijs Rooduijn, Dr.
Department of Political Science
Taught by: Emiel van Loon, Assistant Professor
Institute for Biodiversity and Ecosystem Dynamics
| Basic Info |
Course 3 of 5 in the
Methods and Statistics in Social Sciences Specialization.
|
| Level | Beginner |
| Commitment | 8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week. |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Before we get started...
In this module we'll consider the basics of statistics. But before we start, we'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 ...
1 video, 11 readings, 1 practice quiz
Exploring Data
In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how yo...
8 videos, 5 readings
Graded: Exploring Data
Graded: R lab - Exploring data
WEEK 2
Correlation and Regression
In this second module we’ll look at bivariate analyses: studies with two variables. First we’ll introduce the concept of correlation. We’ll investigate contingency tables (when it comes to categorical variables) and scatterplots (regarding quantitative variabl...
8 videos, 6 readings
Graded: Correlation and Regression
Graded: R lab - Correlation and Regression
WEEK 3
Probability
This module introduces concepts from probability theory and the rules for calculating with probabilities. This is not only useful for answering various kinds of applied statistical questions but also to understand the statistical analyses that will be introduc...
11 videos, 5 readings
Graded: Probability
Graded: R lab - Probability
WEEK 4
Probability Distributions
Probability distributions form the core of many statistical calculations. They are used as mathematical models to represent some random phenomenon and subsequently answer statistical questions about that phenomenon. This module starts by explaining the basic p...
8 videos, 5 readings
Graded: Probability distributions
Graded: R lab - Probability distributions
WEEK 5
Sampling Distributions
Methods for summarizing sample data are called descriptive statistics. However, in most studies we’re not interested in samples, but in underlying populations. If we employ data obtained from a sample to draw conclusions about a wider population, we are using ...
7 videos, 5 readings
Graded: Sampling distributions
Graded: R lab - Sampling distributions
WEEK 6
Confidence Intervals
We can distinguish two types of statistical inference methods. We can: (1) estimate population parameters; and (2) test hypotheses about these parameters. In this module we’ll talk about the first type of inferential statistics: estimation by means of a confid...
7 videos, 4 readings
Graded: Confidence intervals
Graded: R lab - Confidence intervals
WEEK 7
Significance Tests
In this module we’ll talk about statistical hypotheses. They form the main ingredients of the method of significance testing. An hypothesis is nothing more than an expectation about a population. When we conduct a significance test, we use (just like when we c...
7 videos, 4 readings
Graded: Significance tests
Graded: R lab - Significance tests
WEEK 8
Exam time!
This is the final module, where you can apply everything you've learned until now in the final exam. Please note that you can only take the final exam once a month, so make sure you are fully prepared to take the test. Please follow the honor code and do not c...
Graded: Final Exam
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University of Amsterdam
A modern university with a rich history, the University of Amsterdam
(UvA) traces its roots back to 1632, when the Golden Age school Athenaeum
Illustre was established to train students in trade and philosophy. Today,
with more than 30,000 students, 5,000 staff and 285 study programmes
(Bachelor's and Master's), many of which are taught in English, and a
budget of more than 600 million euros, it is one of the largest
comprehensive universities in Europe. It is a member of the League of
European Research Universities and also maintains intensive contact with
other leading research universities around the world.
Pricing
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| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
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