Ce cours fait partie de la Spécialisation Methods and Statistics in Social Sciences

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Spécialisation Methods and Statistics in Social Sciences

University of Amsterdam

À propos de ce cours

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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....

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Recommandé : 8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week....

Sous-titres : English...

StatisticsConfidence IntervalStatistical Hypothesis TestingR Programming

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Réinitialisez les dates limites selon votre disponibilité.

Recommandé : 8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week....

Sous-titres : English...

Week

1In 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 out the 'Course introduction' and 'What to expect from this course' sections 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 ready to get started? Then you might want to skip ahead to the first course topic: 'Exploring data'. You can always check the general information later. Veterans and newbies alike: Don't forget to introduce yourself in the 'meet and greet' forum!...

1 vidéo (Total 4 min), 11 lectures, 1 quiz

Hi there!10 min

How to navigate this course10 min

How to contribute10 min

General info - What will I learn in this course?10 min

Course format - How is this course structured?10 min

Requirements - What resources do I need?10 min

Grading - How do I pass this course?10 min

Team - Who created this course?10 min

Honor Code - Integrity in this course10 min

Useful literature and documents10 min

Research on Feedback10 min

Use of your data for research2 min

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 you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved....

8 vidéos (Total 53 min), 5 lectures, 4 quiz

1.02 Data matrix and frequency table6 min

1.03 Graphs and shapes of distributions7 min

1.04 Mode, median and mean6 min

1.05 Range, interquartile range and box plot7 min

1.06 Variance and standard deviation5 min

1.07 Z-scores4 min

1.08 Example6 min

Data and visualisation10 min

Measures of central tendency and dispersion10 min

Z-scores and example10 min

Transcripts - Exploring data10 min

About the R labs10 min

Exploring Data22 min

Week

2In 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 variables). We’ll also learn how to understand and compute one of the most frequently used measures of correlation: Pearson's r. In the next part of the module we’ll introduce the method of OLS regression analysis. We’ll explain how you (or the computer) can find the regression line and how you can describe this line by means of an equation. We’ll show you that you can assess how well the regression line fits your data by means of the so-called r-squared. We conclude the module with a discussion of why you should always be very careful when interpreting the results of a regression analysis. ...

8 vidéos (Total 49 min), 6 lectures, 2 quiz

2.02 Pearson's r7 min

2.03 Regression - Finding the line3 min

2.04 Regression - Describing the line7 min

2.05 Regression - How good is the line?5 min

2.06 Correlation is not causation5 min

2.07 Example contingency table3 min

2.08 Example Pearson's r and regression8 min

Correlation10 min

Regression10 min

Reference10 min

Caveats and examples10 min

Reference10 min

Transcripts - Correlation and regression10 min

Correlation and Regression20 min

Week

3This 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 introduced in subsequent modules. We start by describing randomness, and explain how random events surround us. Next, we provide an intuitive definition of probability through an example and relate this to the concepts of events, sample space and random trials. A graphical tool to understand these concepts is introduced here as well, the tree-diagram.Thereafter a number of concepts from set theory are explained and related to probability calculations. Here the relation is made to tree-diagrams again, as well as contingency tables. We end with a lesson where conditional probabilities, independence and Bayes rule are explained. All in all, this is quite a theoretical module on a topic that is not always easy to grasp. That's why we have included as many intuitive examples as possible....

11 vidéos (Total 64 min), 5 lectures, 2 quiz

3.01 Randomness4 min

3.02 Probability4 min

3.03 Sample space, event, probability of event and tree diagram5 min

3.04 Quantifying probabilities with tree diagram5 min

3.05 Basic set-theoretic concepts5 min

3.06 Practice with sets7 min

3.07 Union5 min

3.08 Joint and marginal probabilities6 min

3.09 Conditional probability4 min

3.10 Independence between random events5 min

3.11 More conditional probability, decision trees and Bayes' Law8 min

Probability & randomness10 min

Sample space, events & tree diagrams10 min

Probability & sets10 min

Conditional probability & independence10 min

Transcripts - Probability10 min

Probability30 min

Week

4Probability 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 properties of a probability distribution, highlighting how it quantifies a random variable and also pointing out how it differs between discrete and continuous random variables. Subsequently the cumulative probability distribution is introduced and its properties and usage are explained as well. In a next lecture it is shown how a random variable with its associated probability distribution can be characterized by statistics like a mean and variance, just like observational data. The effects of changing random variables by multiplication or addition on these statistics are explained as well.The lecture thereafter introduces the normal distribution, starting by explaining its functional form and some general properties. Next, the basic usage of the normal distribution to calculate probabilities is explained. And in a final lecture the binomial distribution, an important probability distribution for discrete data, is introduced and further explained. By the end of this module you have covered quite some ground and have a solid basis to answer the most frequently encountered statistical questions. Importantly, the fundamental knowledge about probability distributions that is presented here will also provide a solid basis to learn about inferential statistics in the next modules....

8 vidéos (Total 52 min), 5 lectures, 2 quiz

4.02 Cumulative probability distributions5 min

4.03 The mean of a random variable4 min

4.04 Variance of a random variable6 min

4.05 Functional form of the normal distribution6 min

4.06 The normal distribution: probability calculations5 min

4.07 The standard normal distribution8 min

4.08 The binomial distribution8 min

Probability distributions10 min

Mean and variance of a random variable10 min

The normal distribution10 min

The binomial distribution10 min

Transcripts - Probability distributions10 min

Probability distributions30 min

4.7

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par PG•Apr 21st 2016

This is a nice course...thanks for providing such a great content from University of Amserdam.\n\nPlease allow us to complete the course as I have to wait till the session starts for week 2 lessions.

par CD•Mar 6th 2016

This course is really awesome. Designed well. Looks like a lot of efforts have been taken by the team to build this course. Kudos to everyone. Keep up the good work and thank you very much.

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....

Identify interesting questions, analyze data sets, and correctly interpret results to make solid, evidence-based decisions.
This Specialization covers research methods, design and statistical analysis for social science research questions. In the final Capstone Project, you’ll apply the skills you learned by developing your own research question, gathering data, and analyzing and reporting on the results using statistical methods....

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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