1 / 14

Experiments Main role of randomization: Assign treatments to the experimental units.

Why randomize?. Experiments Main role of randomization: Assign treatments to the experimental units. Sampling Main role of randomization: Random selection of the sample of cases from the population. Why randomize?. Experiments Main role of randomization:

kris
Télécharger la présentation

Experiments Main role of randomization: Assign treatments to the experimental units.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Why randomize? • Experiments • Main role of randomization: • Assign treatments to the experimental units. • Sampling • Main role of randomization: • Random selection of the sample of cases from the population.

  2. Why randomize? • Experiments • Main role of randomization: • Assign treatments to the experimental units. • Why? • Avoid bias in which experiment unit gets which treatment. • Sampling • Main role of randomization: • Random selection of the sample of cases from the population. • Why? • Avoid bias in which members of the population gets picked to be measured to get a sample that is representative.

  3. Why randomize? • Experiments • Main role of randomization: • Assign treatments to the experimental units. • Why? • Avoid bias in which experiment unit gets which treatment. • Result: • Can make causal conclusions. • Sampling • Main role of randomization: • Random selection of the sample of cases from the population. • Why? • Avoid bias in which members of the population gets picked to be measured to get a sample that is representative. • Result: • Can generalize observations to the entire population.

  4. Choosing a random sample from the population isn’t enough if some members of the randomly selected don’t respond …

  5. Choosing a random sample from the population isn’t enough if some members of the randomly selected don’t respond …

  6. Simple Random Sample vsStratified Sampling vsCluster Sampling? Which is represented by this diagram?

  7. Correct answer: Stratified sampling

  8. Divide the population into non-overlapping groups. • Stratified sampling • The groups are called strata (singular: stratum). • Within each stratum, pick a simple random sample. • Why? • You want to be sure that each stratum is represented in your sample. • Cluster sampling • The groups are called clusters. • Randomly pick a sample of clusters. The final sample includes every member of each chosen cluster. • Why? • Practical reasons (cost, convenience).

  9. Classic example of stratified sampling: Want to poll all Canadians and want to make sure that you have representation from all provinces. Strata: provinces Classic example of cluster sampling: Want to see how grade 6 students in Toronto public schools respond to an educational program. Clusters: grade 6 classes

  10. The observational units in a stratum should be: Homogeneous Heterogeneous The observational units in a cluster should be: Homogeneous Heterogeneous Correct answer: observational units in a stratum should be homogeneous and in a cluster should be heterogeneous.

  11. The observational units in a stratum should be: Homogeneous Heterogeneous The strata should be different from each other, based on a factor that might influence what is being measured. The observational units in a cluster should be: Homogeneous Heterogeneous The clusters should be similar to each other. And each cluster should be a microcosm of the population.

  12. Correct answer: strata

  13. One final note: In experiments, if you’re going to have a blocking factor, you do this because you expect that the experimental units within blocks are homogeneous when you compare them to the experimental units between blocks. In the shrimp example, consider the 2 levels of the blocking factor before the treatments are assigned. There is heterogeneity in the light (and probably other variables) between the two levels of the blocking factor, but relative homogeneity in light (and these other variables) within each level of the blocking factor.

More Related