Term
Key Terms: define
Homogenous |
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Definition
| all members of a population are/were identical in characteristics |
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Term
Key Terms: define
Heterogeneous |
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Definition
| individual members of a population are different from each other in characteristics |
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Term
Key Terms: define
Sampling |
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Definition
| the process of selecting observations (a sample) to provide an adequate description and robust inferences of the population. the sample is a representative of the population |
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Term
Key Terms: define
Sample element |
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Definition
| A case or a single unit that is selected from a population and measured in some way - the basis of analysis |
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Term
Key Terms: define
Universe with SIUE example |
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Definition
The theoretical aggragation of all possible elements - unspecified to time and space.
ex. SIUE |
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Term
Key Terms: define
Population with SIUE example |
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Definition
The theoretical aggregation of specified elements as defined for a given survey defined by time and space.
ex. SIUE students 2014 |
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Term
Key Terms: define
Sample or target population with SIUE example |
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Definition
The aggregation of the population from which the sample is actually drawn.
ex. SIUE summer students 2014 |
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Term
Key Terms: define
Sample frame with SIUE example |
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Definition
A specific list that closely approximates all elements in the population - from this the researcher selects units to create the study sample.
ex. List of SIUE summer students 2014 |
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Term
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Definition
| A set of cases that is drawn from a larger pool and used to make generalizations about the population |
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Term
Key Concepts
What does sample size depend on? |
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Definition
- How much sampling error can be tolorrated
- Size of population
- varation within the population with respect to the characteristics of interest
- Smallest subgroup within the sample for which estimates were needed
- Sample needs to be big enough to properly estimate the smallest subgroup
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Term
Key Concepts
What are the 4 different types of probability sampling? |
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Definition
- Simple Random Sampling (SRS)
- Systematic random sampling (SS)
- Stratified sampling (StS)
- Cluster sampling
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Term
Key Concepts
Explain Simple Random Sampling (SRS) with method and give example of how this is done |
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Definition
- The basic sampling method which most others are based on.
- Method: a sample size 'n' is drawn from a population 'N' in such a way that every possible element in the population has the same chance of being selected
- ex. Random number table or drawing out of a hat
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Term
Key Concepts
1.Explain Systematic Random Sampling (SS) method. 2.What does the sampling interval do?
3. why is SS better than SRS? |
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Definition
- Starting from a random point on a sampling frame, every 'n'th element in the frame is selected at equal intervals.
- the sampling interval tells the researcher how to select elements from the frame (ex. 1 in 10, 3 in 60), this depends on sample size needed
- Emperically SS provides the same results but it is more efficent
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Term
Key Concepts
Explain Stratified Sampling (StS) method. |
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Definition
| Divide the population by certain characteristics into homogeneous subgroups known as strata(s). A systematic sample is taken from each strata relative to the proportion of that stratum to each of the others. |
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Term
Key Concepts
Cluster sampling
1. when would this type of sampling be used?
2. what do you trade for efficency? |
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Definition
- when researchers lack a good sample frame for a dispersed/spread out population.
- accuracy
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