experimental

The answer is “the experiment”.

Which research method would be most appropriate for investigating the relationship between the religious beliefs of Americans and their attitudes toward abortion? a. case studies.

The main difference between stratified sampling and quota sampling is that stratified sampling would select the students using a probability sampling method such as simple random sampling or systematic sampling. Some units may have no chance of selection or the chance of selection may be unknown.

Cluster sampling offers the following advantages: Cluster sampling is less expensive and more quick. It is more economical to observe clusters of units in a population than randomly selected units scattered over throughout the state. Cluster Sample permits each accumulation of large samples.

K-Means Clustering Advantages and Disadvantages. K-Means Advantages : 1) If variables are huge, then K-Means most of the times computationally faster than hierarchical clustering, if we keep k smalls. 2) K-Means produce tighter clusters than hierarchical clustering, especially if the clusters are globular.

Disadvantages of Cluster Sampling The method is prone to biases. The flaws of the sample selection. If the clusters that represent the entire population were formed under a biased opinion, the inferences about the entire population would be biased as well.

Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed. Researchers usually use pre-existing units such as schools or cities as their clusters.

Snowball Sampling Method The nature of snowball sampling is such, that it cannot be considered for a representative sample or in that case for statistical studies. However, this sampling technique can be extensively used for conducting qualitative research, with a population that is hard to locate.

As sample members are not selected from a sampling frame, snowball samples are subject to numerous biases. For example, people who have many friends are more likely to be recruited into the sample. When virtual social networks are used, then this technique is called virtual snowball sampling.

Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.

The Top 5 Clustering Algorithms Data Scientists Should Know

• K-means Clustering Algorithm.
• Mean-Shift Clustering Algorithm.
• DBSCAN – Density-Based Spatial Clustering of Applications with Noise.
• EM using GMM – Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
• Agglomerative Hierarchical Clustering.

The main advantage of a clustered solution is automatic recovery from failure, that is, recovery without user intervention. Disadvantages of clustering are complexity and inability to recover from database corruption.

The most important limitations of Simple k-means are: The user has to specify k (the number of clusters) in the beginning. k-means can only handle numerical data. k-means assumes that we deal with spherical clusters and that each cluster has roughly equal numbers of observations.