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Archive for November, 2010

Sample Size and Power Analysis

November 5, 2010 Comments off

Each of these four components of your study (sample size, statistical power, effect size, and significance level) are a function of the other three, meaning that altering one causes changes in the others.

Sample size is critical to ensuring the validity of your study and should be determined in the very early stages of study design The effect size of your study is critical; this unique measurement will tell you the strength or importance of a particular relationship.

Power is the measurement of the probability of committing a Type II error, which is the probability of not finding a relationship that exists in your analysis. The a priori power is unique to every study.

The alpha or significance level of your study is the probability of committing a Type I error. More simply, it is the probability of finding a relationship that does not exist. Generally, committing a Type I error is considered more severe than committing a Type II error.

The significance level measurement is unique to your study. The significance level for a study involving airbag deployment failures would not be the same as the significance level for a study involving the satisfaction of five-year-old children with a particular brand of red crayon.

via Sample Size and Power Analysis | Statistics Solutions.

Getting the Sample Size Right: A Brief Introduction to Power Analysis Link

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Non-probability Sampling

November 1, 2010 Comments off

The difference between non-probability and probability sampling is that non-probability sampling does not involve random selection and probability sampling does. Does that mean that non-probability samples aren’t representative of the population? Not necessarily. But it does mean that non-probability samples cannot depend upon the rationale of probability theory. At least with a probabilistic sample, we know the odds or probability that we have represented the population well. We are able to estimate confidence intervals for the statistic. With non-probability samples, we may or may not represent the population well, and it will often be hard for us to know how well we’ve done so. In general, researchers prefer probabilistic or random sampling methods over non-probabilistic ones, and consider them to be more accurate and rigorous. However, in applied social research there may be circumstances where it is not feasible, practical or theoretically sensible to do random sampling. Here, we consider a wide range of non-probabilistic alternatives.

For more detail follow this link: Non-probability Sampling.

Categories: First Principles, Sampling

Probability Sampling

November 1, 2010 Comments off
The chosen "random" sample

Image by Marco De Cesaris via Flickr

A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection.

For more detail follow this link: Probability Sampling.