Summary Designing Surveys: A Guide to Decisions and Procedures

ISBN-10 1412997348 ISBN-13 9781412997348
314 Flashcards & Notes
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This is the summary of the book "Designing Surveys: A Guide to Decisions and Procedures". The author(s) of the book is/are . The ISBN of the book is 9781412997348 or 1412997348. This summary is written by students who study efficient with the Study Tool of Study Smart With Chris.

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What is imputation?
The substitution of constructed values for items that are not answered or whoe answers are inconsistent with other responses in the same interview. 
What are the three principal forms of postsurvey statistical adjustments?
1. Weighting to adjust for unequal selection probabilities in the sample design.
2. Weighting to adjust for differential unit nonresponse accros population subgroups.
3. Imputation for item nonresponse. 
What are two general methods for assessing exporue to nonresponse bias?
1. Compare the survey data with external data.
2. Examine internal variation in the data collection.
How can duplication and clustering occur in counting frames?
Duplication and clustering usually result from a mismatch between the counting units and population units. 
How can ineligibility occur in counting frames?
Ineligibility results from some of the counted elements not meeting population criteria.
How can omisson occur in counting frames?
Omission results from underestimating the population size (or the population subgroups). 
How is sampling without lists done?
1. Estimate the size of the population.
2. Select a sample of numbers between 1 and N, where N is the population size.
3. Count the population and gather data from the appropriately numbered members. 
What are three basic ways to cope with clustering?
1. Gather data from all population elements in the selected clusters. This method provides a fair chance of selection for every member of the population. Unfortunately, it also produces a sample that contains related cluster members. Because of this problem, taking entire clusters is a good idea only when clusters are relatively small and relatively few in number.
2. Sample population members within clusters at some fixed rate. 
3. Compensate for clustering by randomly selecting one population member from each cluster and weighting that observation by the size of the cluster. 
What is the problem with clustering?
Clusterd members are underrepresented in the sample. 
What are three ways to correct for duplicate listings?
1. To cross-check the list, identify duplicated, and remove them. This will be done if the list is computerized. Computerized cross-checking usually doesn't remove all of the duplications in a list, but it should reduce them to a level where they cause negligible sample bias. 
2. To draw the sample and check only the selected elements to determine how many times they are duplicated in the total population list. Sample members that are listed k times would be retained at the rate of 1/k. 
3. To ask selected population members how many times they appear in the lit. Observations are weighted by the inverse of their number of times in the list (1/k).