Rendition (1)
Sample Surveys
Sample surveys involve collecting data from a subset of a population to make inferences about the entire population.
Errors can lead to bias in survey results.
Types of Errors in Sample Surveys
Two main types of errors: sampling errors and non-sampling errors.
Sampling Errors
Defined as errors caused by the act of taking a sample.
A sampling error results from the deviation of the selected sample from the true characteristics of the population.
Caused by:
Random sampling causes minor errors due to chance.
Bad sampling methods (e.g., voluntary response sampling, convenience sampling).
Random Sampling Error
Results from chance deviations between the sample statistic and the population parameter.
Can be controlled by increasing the sample size.
Non-Sampling Errors
Errors unrelated to selecting a sample; more difficult to manage and quantify.
Frame Errors
Occur when the sampling frame does not accurately represent the population.
Example: Using telephone directories for surveys leads to undercoverage.
Undercoverage excludes certain groups:
Those with unlisted landline numbers.
Individuals without landline phones (e.g., low-income households, young people).
Those only having cell phones.
To mitigate, use random digit dialing to achieve a better sample.
Nonresponse Errors
The failure to obtain data from selected individuals for a sample.
Considered the most serious problem in sample surveys.
Managed through substitution:
Replace nonrespondent households with other households from the same neighborhood.
Response Errors
Occurs when respondents provide incorrect answers, often due to a desire to appear favorable.
Example: Individuals lying about alcohol consumption or smoking habits.
To reduce, ask sensitive questions carefully, ensuring confidentiality.
Processing Errors
Mistakes in mechanical tasks, such as calculations or data entry.
The use of computers has reduced the frequency of these errors.
Sample Surveys: Collect data from a subset to infer about the entire population. Errors can lead to bias.
Types of Errors:
Sampling Errors: Caused by the act of taking a sample; deviations from true population characteristics.
Caused by random chance and poor sampling methods (e.g., convenience sampling).
Random Sampling Error: Can be mitigated by increasing sample size.
Non-Sampling Errors: Not related to sample selection; harder to manage.
Frame Errors: Sampling frame misrepresents the population (e.g., using phone directories).
Undercoverage: Excludes certain groups; can be improved with random digit dialing.
Nonresponse Errors: Missing data from selected individuals; most serious problem. Substitute when possible.
Response Errors: Incorrect answers due to social desirability. Mitigate through careful questioning.
Processing Errors: Mistakes in calculations/data entry; less common due to computer use.