5.04 Probability Sampling - Simple

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Из курса от партнера University of Amsterdam

Quantitative Methods

903 оценки

University of Amsterdam

Quantitative Methods

903 оценки

Курс 1 из 5 — Specialization Methods and Statistics in Social Sciences

Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research! This course will cover the fundamental principles of science, some history and philosophy of science, research designs, measurement, sampling and ethics. 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.

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Sampling

In the previous two modules we discussed research designs and methods to measure and manipulate our variables of interest and disinterest. Before a researcher can move on to the testing phase and can actually collect data, there is just one more procedure that needs to be decided on: Sampling. Researchers need to determine who potential participants are and how they will be selected and recruited.

5.03 Probability Sampling4:33

5.04 Probability Sampling - Simple4:02

5.05 Probability Sampling - Complex5:44

5.06 Non-Probability Sampling5:39

Познакомьтесь с преподавателями

Annemarie Zand Scholten
Assistant Professor
Economics and Business

There are several types of probability sampling.

In this video, I'll discuss the two simplest types, simple random sampling and

systematic sampling.

The most basic form of probability sampling is simple random sampling.

In simple random sampling, each element in the sampling frame has an equal and

independent probability of being included in the sample.

Independent means the selection of any single element does not depend on

another element being selected first.

In other words,

every possible combination of elements is equally likely to be sampled.

To obtain a simple random sample, we could write every unique combination of sampled

elements on a separate card, shuffling the cards, and then blindly drawing one card.

Of course, if the population is large,

then writing out all possible combinations is just too much work.

Fortunately, an equivalent method is to randomly select individual elements.

This can be done using random number tables still found in the back of

some statistics books, but these tables have come obsolete.

We can now generate random number sequences with a computer.

For example, if our population consists of 12 million registered taxpayers, then we

can generate a sequence of 200 unique random numbers between one and 12 million.

Systematic sampling is a related method aimed to obtain a random sample also.

In systematic sampling, only the first element is selected using a random number.

The other elements are selected by systematically skipping a certain number

of elements.

Suppose we want to sample the quality of cat food on an assembly line.

A random number gives us a starting point, say, the seventh bag.

We then sample each tenth bag.

So we select bag number seven, 17, 27, etc.

It would much harder to select elements according to random numbers, say,

bag number seven, 30,

36, 41, et cetera, especially if the assembly line moves very fast.

With this approach, each element has an equal probability of being selected, but

the probabilities are no longer independent.

Element 17, 27, 37,

et cetera, are only chosen if seven is chosen as the starting point.

This is not a real problem.

It just requires a little more statistical work to determine things like

the margin of error.

The real problem with systematic sampling is that it only results in a truly random

sample if there's absolutely no pattern to the list of elements.

What if the assembly line alternately produces cat food made with fish and

cat food made with beef?

Let's say all odd-numbered elements are made with fish.

In our example, we would never sample the quality of cat food made with beef.

Of course, this an exaggerated example, but

it illustrates that systematic sampling can be dangerous.

A preexisting list or ordering of elements can always contain a pattern that

were unaware of, resulting in a biased sample.

So, systematic sampling only results in a truly random sample if

it's absolutely certain that the list of elements is ordered randomly.

We can make sure of this by randomly reordering the entire list.

We can generate a sequence of random numbers of the same size as the list and

then select elements from this list using systematic sampling.

Of course, this is equivalent to random selection directly from

the original list using random numbers.

Unless we can be sure that the list is truly random,

systematic sampling should not be considered a form of probability sampling.

Instead, it should be considered a form of non-probability sampling.

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