Foundations of Sociological Inquiry The Logic of Sampling

Today’s Objectives  History of Sampling  Nonprobability Sampling  Probability Sampling  Populations and Sampling Frames  Sampling Designs  Multistage Cluster Sampling

History of Sampling  Unemployment  Politics  Poverty

In the past week were you employed? 1. Yes 2. No

Please answer ONLY if you are working or looking for work. In the past week were you employed? 1. Yes 2. No

Nonprobability Sampling  any technique in which samples are selected in some way not suggested by probability theory.  Available subjects  Purposive sampling  Snowball sampling  Quota sampling

In her research project, Ella wants to study the processes involved in lesbian partners adopting children. She starts off by interviewing lesbian couples who have adopted in the past, and they in turn, give her names of other lesbian couples who have adopted. 1. quota sampling. 2. convenient sampling. 3. snowball sampling. 4. systematic sampling. 5. judgmental sampling. The process by which Ella gets her sample is called

Probability Sampling  the general term for samples selected in accord with probability theory.  Often used for large-scale surveys  Also used for other types of quantitative data collection/analysis  A sample of observations from a population must contain the same variations that exist in the population (representative)  A sample will be representative of the population from which it is selected if all members/elements of the population have an equal chance of being selected in the sample

A _______ is the list of elements from which a probability sample is selected. 1. confidence interval 2. confidence level 3. sampling frame 4. systematic sample

Populations and Sampling Frames  Population – the theoretically specified aggregation of the elements in a study  Sampling Frame – a list of units that compose a population from which a sample is selected.

Research suggests that samples of respondents drawn from people living in households are: 1. More likely to be employed than those not living in households 2. Less likely to be employed than those not living in households 3. I don’t know

Populations and Sampling Frames  Statistics can be used to:  Summarize the characteristics of the sample (summary)  Estimate population parameters (inference)  Frequentist (assumes the population is known, or at least from a known distribution, to make statements of inference/uncertainty)  Bayesian (uses the observed data to generate statements of inference/uncertainty)

A summary description of a variable in a sample is called a 1. variable. 2. parameter. 3. confidence level. 4. confidence interval. 5. statistic.

Populations and Sampling Frames  Sampling Error – the degree of error to be expected of a given sample design.  Confidence Levels and Confidence Intervals  Confidence Level – the estimated probability that a population parameter lies within a given confidence interval.  Confidence Interval – the range of values within which a population parameter is estimated to lie.

_____ of people fall within TWO standard deviations of a normal distribution % 2. 90% 3. 95% % 5. 34%

Sampling Designs  Simple Random Sampling  a type of probability sampling in which the units composing a population are assigned numbers. A set of random numbers is generated and the units having those numbers are included in the sample.  Systematic Sampling  a type of probability sampling in which every k th unit in a list is selected for inclusion in the sample.  Stratified Sampling  Stratification – the grouping of units composing a population into homogenous groups (strata) before sampling  Implicit Stratification in Systematic Sampling  a multistage sampling in which natural groups are sampled initially with the members of each selected group being sub-sampled afterward.

You are doing research on hospital personnel—orderlies, technicians, nurses, and doctors. You want to be sure you draw a sample that has cases in each of the personnel categories. You want to use probability sampling. An appropriate strategy would be 1. simple random sampling. 2. quota sampling. 3. cluster sampling. 4. stratified sampling. 5. accidental sampling.

Which of the following statements about stratifying a population prior to drawing a sample is TRUE? 1. Stratifying a population prior to drawing a sample eliminates the need for simple random sampling. 2. Stratifying a population prior to drawing a sample is most useful for studying a homogeneous population. 3. Stratifying a population prior to drawing a sample eliminates the need for probability sampling. 4. Stratifying a population prior to drawing a sample is an alternative to either random or systematic sampling. 5. All of these statements are FALSE.

Cluster sampling may be used when it is impossible to compile an exhaustive list of the elements composing the target population. 1. True 2. False

Questions?