Final Exam Study - Experiments, Factorial Designs, and Sampling
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| What would your hypothesis look like if you had an experiment? | It would involve a single independent variable where the goal of the experiment would be to compare the observations for the different levels of the independent variable.
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| What is an interaction effect? | Tests the effect of one independent variable for each level of another independent variable to determine how the IVs interact to effect the dependent variable. Compares the differences of the levels of one IV across the levels of another IV.
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| What is a simple random sample technique? | Each member of the population has an equal probability of being selected using random sampling. E.g. student are chosen randomly from list of all student at a university
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| Pros of simple-random sample techniques | Reduces sampling error by choosing from all members of the population to best represent the population
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| Cons of simple-random sample techniques | Difficult to ensure that each member of a large population can be chosen in a sample
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| What is a cluster sampling technique? | Clusters of individuals are identified and then a subset of clusters is randomly chosen to sample from.
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| Example of a cluster sampling technique | Doctors who work at hospitals are chosen for a sample by identifying all hospitals in different areas of the US and then randomly choosing 10 hospitals in each area of the US to sample from
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| Pros of cluster sampling | Makes it easier to choose members randomly from smaller clusters to better represent the population
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| Cons of cluster sampling | Can ignore segments of the population that aren't in the clusters chosen for the sample
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| What are stratified random samples? | Members of a population are selected such that the proportion of a group in the sample is equal to the proportion of that group in the population using random sampling
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| Example of a stratified random sample technique | Registered voters are randomly selected from lists of democrats and republicans to equal the proportion of registered democrats and republicans in the US
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| Pros of stratified random samples | Reduces bias due to an identified characteristic of the population by equating proportions in the sample and the population for that characteristic to better represent the population
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| Cons of stratified random samples | Similar to simple random - can be difficult to ensure equal probability of being chosen from a large population
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| What is haphazard/volunteer sampling? | Members of a population are chosen based on convenience and on who volunteers
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| Example of haphazard/volunteer sample | Sample is chosen from students who volunteer to complete an extra credit assignment in their psych course.
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| Pros of haphazard/volunteer sampling | Easier to obtain than probability samples
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| Cons of haphazard/volunteer sampling | May not represent the population well due to selection bias because random sampling is not used
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| What is a quota sampling technique? | Members of population are selected such that the proportion of a group in the sample is equal to proportion of that group in population
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| Example of quota sampling | Volunteers are recruited from students in a psychology course such that the gender breakdown in the sample is the same as the gender breakdown of all students at the school
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| Pros of quota sampling | Easier to obtain that probability samples and allows for better representation of a characteristic of the population
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| Cons of quota sampling | may not represent the population well due to selection bias because random sampling is not used
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