What is a critical case study?
In critical (or theoretical) case study the researcher deliberately selects, for detailed empirical analysis, a case that provides a specific focus for analysis of myth or contradiction. A variety of different data collection techniques can be adopted within a critical case study approach.
What is extreme case sampling?
Extreme or deviant case sampling means selecting cases that are unusual or special in some way, such as outstanding successes or notable failures.
What is a purposive sampling technique?
Purposive sampling, also known as judgmental, selective, or subjective sampling, is a form of non-probability sampling in which researchers rely on their own judgment when choosing members of the population to participate in their study.
What are the types of purposive sampling?
Types of purposive sampling
- Maximum variation sampling.
- Homogeneous sampling.
- Typical case sampling.
- Extreme (or deviant) case sampling.
- Critical case sampling.
- Total population sampling.
- Expert sampling.
What is the basis for all types of sampling distribution?
That’s the basis behind a sampling distribution: you take your average (or another statistic, like the variance) and you plot those statistics on a graph. This video introduces the Central Limit Theorem as it applies to these distributions.
How do you find mean of sampling distribution?
For samples of any size drawn from a normally distributed population, the sample mean is normally distributed, with mean μX=μ and standard deviation σX=σ/√n, where n is the sample size.
How do you create a sampling distribution?
To create a sampling distribution a research must (1) select a random sample of a specific size (N) from a population, (2) calculate the chosen statistic for this sample (e.g. mean), (3) plot this statistic on a frequency distribution, and (4) repeat these steps an infinite number of times.
What does a sampling distribution represent?
The sampling distribution of a given population is the distribution of frequencies of a range of different outcomes that could possibly occur for a statistic of a population. It describes a range of possible outcomes that of a statistic, such as the mean or mode of some variable, as it truly exists a population.
What makes a sampling distribution normal?
The central limit theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal, if the sample size is large enough.
Is the sampling distribution always normal?
In other words, regardless of whether the population distribution is normal, the sampling distribution of the sample mean will always be normal, which is profound! The central limit theorem (CLT) is a theorem that gives us a way to turn a non-normal distribution into a normal distribution.
How do you know if a sample is normally distributed?
The central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.
What does the 10 Condition say about the sample?
The 10% condition states that sample sizes should be no more than 10% of the population. Whenever samples are involved in statistics, check the condition to ensure you have sound results. Some statisticians argue that a 5% condition is better than 10% if you want to use a standard normal model.
How do you know if it is a sample or population?
A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. In research, a population doesn’t always refer to people.
What does the sample mean tell us?
The sample mean from a group of observations is an estimate of the population mean . Each of these variables has the distribution of the population, with mean and standard deviation . The sample mean is defined to be .
Is the sample mean biased?
More formally, a statistic is biased if the mean of the sampling distribution of the statistic is not equal to the parameter. The mean of the sampling distribution of a statistic is sometimes referred to as the expected value of the statistic. Therefore the sample mean is an unbiased estimate of μ.
Is the sample mean consistent?
The sample mean is a consistent estimator for the population mean. A consistent estimate has insignificant errors (variations) as sample sizes grow larger. More specifically, the probability that those errors will vary by more than a given amount approaches zero as the sample size increases.