lecture 7 sampling

8/12/2019 Lecture 7 Sampling

1/21

Sampling for Surveys


2/21

Surveys

What is a survey?

A process of presenting a standard series of questions to asample of persons.

The survey is the most widely used technique in criminologybecause it is best suited for looking at the complex social world.To capture that world accurately, we have to measure itin situ.

That means taking information from selected people,from where they are usually found.

Measures of many phenomena of interest are taken. Thepurpose is to accurately reflect the beliefs, attitudes, andbehaviors of the sample in order to generalize accurateinformation to a target population.


3/21

Surveys

Survey research typically uses sampling rather than takingcensus.Sampling vs. Taking a Census Sampling: selecting cases (elements)or locating people (or other units of

analysis)from a target population in order to study the population.

Taking a Census: using all cases in an entire target (all elements)in order tostudy the population

So why dont we always take a census?

ASampleis a: Noun: the group from whom data are (or were) gathered, and

Verb: to select cases that represent a populationnot a musical term here

There are multiple ways to sample, but the goal is for thesample to maximally represent the target population


4/21

Sample vs. Population

Population

Sample


5/21

Sampling

Types of Samples (Multiple units of analysis can be sampled):

Cases Persons in Field Studies Situations

Archival Data Experiment Participants

Persons answering a Survey

Depending on how the sample was generated, there are limitsto how much findings can be generalized from it.

One aims for broad generalizability, but type of sampling isalso determined by the:

complexities of the target population, and

researchersresources


6/21

Sampling

Sampling Techniques

Nonprobability: Sampling methods that do not let usknow in advance the likelihood of selecting for the

sample each element or case from a populationvs.

Probability: Sampling methods that allow us to knowin advance how likely it is that any element of apopulation will be selected for the sample

Knowing the chance of selection allows one to controlsampling bias (under or overrepresentation of apopulation characteristic in a sample)


7/21

Nonprobability Sampling Nonprobability

(Very common in psychology, medicine, sociology)

1. Availability Sampling, convenience sampling

Selection of cases based on what is easiest to do Experiments

Exploratory and Qualitative research

Avoid this if you can

2. Quota SamplingAspects of target population are known. Selectsavailability sample ensuring that it reflects knownaspects of population


8/21

Nonprobability Sampling

3. Snowball SamplingRespondent-driven sampling, initial respondents referothers to the researcher Usually used with hard-to-discover populations

Bias introduced by structured nature of affiliation Can be improved with incentives to subjects to recruit a certain

number of new respondents

4. Purposive SamplingTargets select people for a sample because of their

unique position Helps get understanding of systems or processes or

information on a target population Not representative of population in general


9/21

Nonprobability Sampling

Critiques

Limited generalizabilityone cannot judge representativeness.

Researchers should estimate who the sample represents . . . The

sample at least represents populations that are similar to it.

Why use nonprobability samples? Nonprobability does not mean,intentional attempt to get a sample that is not representative:

1. Well-suited for exploratory and evaluation research2. Sampling frames (lists from which samples are drawn) are at

times inadequate or nonexistent

3. Quick, efficient4. Can be effectively used to study and describe social and socialpsychological processes

5. Any research is limited, but not having research is worse.6. Across samples, repeatedly finding the same results supports

generalizability.


10/21

Probability Sampling

Sampling Techniques

Probability Sampling: Sampling method reveals in advance thelikelihood that any one element will be selected for the sample

Probability sampling begins with a sampling frame, or a list of all

elements (or other units containing the elements) in a population.

E.g., Phone book, All Universities, Known Addresses, Subscribers to amagazine.

If a sampling frame is incomplete (which they usually are) then the accuracyof the sample is compromised. The researcher has the burden of assessing

the sampling error or bias.


11/21


1. Simple Random Sampling

Cases are identified strictly on the basis of chance. Random number table to select from sampling frame

Random digit dialing Equal probability of selection

2. Systematic Random Sampling

First case selected randomly from list, subsequent

cases are selected at equal intervals. Typically the same as Simple Random Sampling

Be aware of periodicity


12/21


3. Cluster Sampling

Use when sampling frame is difficult to obtain, butclusters are identifiable.

Randomly select clusters, then use obtainablesampling frames within the clusters to selectcases.

Example: There is no national list of independent Baptists,but almost all independent Baptist churches can beidentified. Members can be selected from membership lists.

Because clusters are generally homogeneous(e.g., all white churches) it is better to maximizethe number of clusters and minimize numberof cases from each cluster


13/21


Multistage cluster sampling

Selecting clusters in two or more hierarchicalstages(e.g., selecting states, then selecting churches,then members)

Keep stages to a minimum because each stageproduces sampling error; more stages, more error


14/21


4. Stratified Random Sampling

Sampling frame divided into strata, cases drawn fromeach stratum randomly.

Small subpopulations of interest may yield too fewcases in simple random sampling. Tocompensate, the researcher draws samples fromeach subpopulation independently.

Example: Latino population of Santa Clara County isaround 25%. A random sample of 100 wouldproduce 20 30 Latinostoo few to generalize toSanta Clara County Latinos.


15/21


4. Stratified Random Sampling

Proportionate Stratified SamplingEnsuring that population proportions are reflected inproportions of each stratum of sample.

Population: 4% black, 25% Latino, 27% Asian, 44% white Sample of 1,000: 40 black, 250 Latino, 270 Asian, 440 white

Disproportionate Stratified SamplingPopulation proportions are NOT reflected inproportions of each stratum of sample. Population: 4% black, 25% Latino, 27% Asian, 44% white Sample of 1,000: 250 black, 250 Latino, 250 Asian, 250 white Idea is to get a lot of cases in each stratum When combining all cases into one sample, use weighted

averages


16/21

2010 GSS Sampling

Full probability sample of US householdseachhousehold has an equal chance of beingselected

Used stratified area probability sampling At the household level, 1 adult is selected at random

(Kish Table)

Sampling frame Most cases came from a list of addresses from USPS (over 2/3)

Remaining cases from NORC-generated lists of households


17/21

2010 GSS SamplingStages used in four population area types, ending with random adult

1.Big MSAs (city), have USPS address42% of population1. Primary sampling unit: tract (1 -2K Housing units)168 selected

2. Housing Unit Selected from USPS List

2.Intermediate MSAs or counties, have USPS address30% of population

1. Primary sampling unit: MSA or part of county30 selected2. Secondary sampling unit: tract120 selected

3. Housing unit Selected from USPS List

3.Rural counties and Intermediate areas (2) without adequate USPS address list25% of population

1. Primary sampling unit: County, all or part25 selected

2. Secondary sampling unit: Segment (constructed to contain 300 Housing Units)100 selected

3. Housing unit from NORC-listed master

4.Big MSAs (city), without adequate USPS address list3% of population1. Primary sampling unit: Segment12 selected

2. Housing unit from NORC-listed master


18/21

2010 GSS Sampling

Source: http://www.fcsm.gov/03papers/keynotespeaker.pdf, January 12, 2012

Stratum 3 =NORC list used
http://www.fcsm.gov/03papers/keynotespeaker.pdfhttp://www.fcsm.gov/03papers/keynotespeaker.pdf


19/21

2010 GSS Sampling

What are the implications of the General SocialSurveys sampling???

The GSS is an adults-only survey of persons in

households. Therefore, it underrepresents: 18 24 year-olds (many not living in households

military, college, roaming)

65 and over (many not living in householdsvacations, RVs, assisted living)

Persons who live in large households (only oneperson per household is interviewed)

Homeless, criminal, and some poor (not in officialhouseholds, in shelters, on streets, in apartments)


20/21


Critiques Just being random does not ensure that a sample is

representative or that the research is good.

Limited Sampling Frame

Think of presidential phone polls:

Who is at home? Type of person, day of polling, etc.

Who has a land line?

Problems of non-responserandom non-response okay,but systematic non-response is biasing

Phone surveys typically do not report response rate. They

are often below 30%

How were questions worded: Measurement error

Problems of misspecified models: Leads to not asking theright questions


21/21


Critiques

Is the Sample large enough? Larger samples produce less sampling error

Too large is a waste of money

Big is good, but accurate and appropriate are better

Fraction of population sampled does not increase accuracyunless fraction is very large

Larger samples are needed when:

The population is more heterogeneous.

There are more variables of interest. The weaker the effects, or the smaller the differences

between groups,

TO SUM: MORE COMPLEXITY REQUIRES LARGER SAMPLES

lecture 7 sampling

Documents