Ch 4 Sampling How to Select a Few

Ch. 4, Sampling: How to Select a Few to Represent the Many (Pt. 1) Neumann, pp. 86 -105. 1

HOW AND WHY DO SAMPLES WORK? n A proper, representative sample lets you study features of the sample and produce highly accurate generalizations about the entire population 2

The most representative samples use random selection n The random process allows us to build on mathematical theories about probability n Due to their use of random selection, probability samples are also called random samples 3

Sample, population, random sample n sample: a small collection of units taken from a larger collection n population: a larger collection of units from which a sample is drawn n random sample: a sample drawn in which a random process is used to select units from a population 4

Sampling in qualitative research n Qual & quant researchers both use sampling, but qualitative researchers have different goals than to get a representative sample of a large population n Qualitative researchers believe a small collection of cases, units, or activities can illuminate key features of an area of social life n n Use sampling less to represent a population than to highlight informative cases, events, or actions Goal is to clarify and deepen understanding based on highlighted cases 5

FOCUSING ON A SPECIFIC GROUP: 4 TYPES OF NONRANDOM SAMPLES n Random samples are difficult to conduct n Researchers who cannot draw random samples use nonprobability sampling techniques n n Convenience sampling Quota sampling Purposive or judgmental sampling Snowball sampling 6

Convenience sampling n convenience sampling: a nonrandom sample in which you use a nonsystematic selection method that often produces samples very unlike the population it’s cheap and fast, but of limited use n with caution, can be used for preliminary phase of an exploratory study n n also called accidental or haphazard sampling 7

Quota sampling n quota sampling: nonrandom sample in which you use any means to fill preset categories that are characteristics of the population n Not as accurate as a random sample, but much easier and faster 8

Quota sampling: in steps 1) Identify several categories of people or units that reflect aspects of diversity in population you believe to be important -e. g. , gender or age 2) Decide how many units to get for each category, i. e. , what the “quota” will be 3) Select units by any method 9

Purposive or judgmental sampling n purposive sampling: a nonrandom sample in which you use many diverse means to select units that fit very specific characteristics n It’s like convenience sampling for a highly targeted, narrowly defined population n Used in 2 types of situations: 1) to select especially informative cases 2) to select cases from a specific but hard-toreach population 10

Snowball sampling n snowball sampling: a nonrandom sample in which selection is based on connections in a preexisiting network It is a multistage technique n Each person or case has a connection with the others n n also called network, chain-referral or reputational sampling 11

Examples of networks studied using snowball sampling n Scientists around world investigating same issue n Elites of a medium-sized city who consult with one another n Drug dealers & suppliers in a distribution network n People on a college campus who have had sexual relations with one another 12

COMING TO CONCLUSIONS ABOUT LARGE POPULATIONS n sampling element: a case or unit of analysis of the population that can be selected for a sample n e. g. , a person, a group, an organization, a written document or symbolic message, or a social action or event (e. g. , an arrest, a protest event, divorce, a kiss) 13

Universe, population, and target population – increasing degrees of specificity n universe: the broad group to whom you wish to generalize your theoretical results n e. g. , all people in FL n population: a collection of elements from which you draw a sample n e. g. , all adults in the Miami metro area n target population: the specific population that you used n e. g. , all adults who had a permanent address in Dade country, FL in Sept 2007, and who spoke English, Spanish, or Haitian Creole 14

Use target population to create a list of its sampling elements, a sampling frame n sampling frame: a specific list of sampling elements in the target population n population parameter: any characteristic of the entire population that you estimate from a sample n sampling ratio: the ratio of the sample size to the size of the target population 15

Why use random samples? n They’re most likely to produce a sample that truly represents the population n True random processes: 1) 2) are purely mechanical or mathematical without human involvement allow us to calculate the probability of outcomes with great precision 16

All samples contain a margin of error n A random process makes it possible to estimate mathematically the degree of match between sample and population, or sampling error n sampling error: the degree to which a sample deviates from a population 17

Key features of random samples 1) They’re based on an accurate sampling frame 2) They use a random selection process without subjective human decisions 3) They rarely use substitutions for sampling elements 18

Types of random samples n Simple random samples n Systematic sampling n Stratified sampling n Cluster sampling 19

Simple random samples n In simple random sampling: n n n First develop an accurate sampling frame Select elements from the frame based on a mathematically random selection procedure Locate the exact selected elements to be in your sample 20

Over many separate samples, the true population parameter is the most frequent result n sampling distribution: a plot of many random samples, with a sample characteristic across the bottom and the number of samples indicated along the side n The sampling distribution shows the same bell- shaped pattern whether your sample size is 1000 or 100 n but the more samples drawn, the clearer the pattern 21

Example of sampling distribution n Number of blue & white marbles that were randomly drawn from a jar of 5, 000 marbles with 100 drawn each time, repeated 130 times for 130 independent random samples Blue marbles White marbles # of samples 42 58 1 43 57 1 45 55 2 46 54 4 47 53 8 48 52 12 49 51 21 50 50 31 51 49 20 52 48 13 53 47 9 54 46 5 55 45 2 57 43 1

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Systematic sampling n If you lack tools to select a pure random sample, systematic sampling is a quasirandom method n systematic sampling: an approximation to random sampling in which you select one in a certain number of sample elements; the number is from the sampling interval n sampling interval: the size of the sample frame over the sample size, used in systematic sampling to select units 24

Stratified sampling n Sometimes researchers want to include specific kinds of diversity in their sample, e. g. , racial diversity n stratified sampling: a type of random sampling in which a random sample is drawn from multiple sampling frames, each for a part of the population n Because you control the relative size of each stratum rather then letting random processes control it, you can be sure your sample will be representative of strata n Stratified sampling generally results in a slightly more representative sample than simple random sampling 25

Selecting a stratified sample 1) Divide population into subpopulations (strata) -To use this method, you must have info about strata in population (i. e. , the population parameter). 2) Create multiple sampling frames, one for each subpopulation 3) Draw random samples, one from each sampling frame 26

Cluster sampling n In some situations where there is no good sampling frame, you can use multiple-stage sampling with clusters n A cluster is grouping of the elements in the final sample that you are interested in n cluster sampling: a multistage sampling method in which clusters are randomly sampled, and then a random sample of elements is taken from sampled clusters 27

THREE SPECIALIZED SAMPLING SITUATIONS n Random-Digit Dialing (RDD) n Within-Household Sampling n Sampling Hidden Populations 28

Random-digit dialing n random-digit dialing: computer based random sampling of telephone numbers 29

Within-household sampling n A household can be thought of as a cluster in which there can be multiple sampling elements or individuals n To ensure random selection, create selection rules, and follow them consistently 30

Sampling hidden populations n hidden population: a group that is very difficult to locate and may not want to be found and is therefore difficult to sample 31