Ch 11 Sampling

The Nature of Sampling

• Sampling• Population Element• Population• Census• Sampling frame

Why Sample?

Greater accuracy

Availability of elements

Greater speed

Sampling provides

Lower cost

When Is a Census Appropriate?

NecessaryFeasible

What Is a Valid Sample?

Accurate Precise

Types of Sampling DesignsElement Selection

Probability Nonprobability

Unrestricted Simple random Convenience

Restricted Complex random Purposive

Systematic Judgment

Cluster Quota

Stratified Snowball

Double

When to Use Larger Sample Sizes?

Desired precision

Number of subgroups

Confidence level

Population variance

Small error range

What is Sample

• Sampling1. Any group on which information is obtained2. Usually is representative of a larger group called a population3. Defining the population --- The group of interest to the researcher --- A group researcher would like to generalize --- Usually has at least one characteristic that sets it off from other populations --- The target population may not have the characteristics one would like

to generalize from --- The accessible population is the population one could generalize from

Random Sampling

• Definition1. Obtaining a rather accurate representative view of a larger group2. Every individual in the population has an equal opportunity to be selected3. When each member of the population does not have a chance of being selected because the researcher is looking for specific criteria it is an example of a nonrandom sample or purposeful sample

Random Sampling (Continued 1)

• Random Sampling Methods1. Simple Random Sampling --- Every member of the population has an equal and independent chance of being selected --- This done by using a table of random numbers which is a

large list of numbers that has no order or pattern --- The list is usually focused in the back of statistics books --- The purpose of random sampling is that if it is large enough it should produce a representative sample

Simple Random

Advantages• Easy to implement

with random dialing

Disadvantages• Requires list of

population elements• Time consuming• Uses larger sample

sizes• Produces larger

errors• High cost

Random Sampling (Continued 2)

--- A random sample needs to be more than 20 to 30 individuals to be large enough to be representativea. Descriptive studies: 100b. Correlational studies: 50c. Experimental studies: 30d. Causal-comparative: 30

2. Stratified Random Sampling --- Strata-sub groups are selected for the sample in the same proportions

as they exist in the population --- Selects a representative or equal percentage from each strata using the table of random numbers --- This will improve the likelihood that the key characteristics the researcher is wising to make generalizations about will be included proportionately in the sample

Stratified

Advantages• Control of sample size in

strata• Increased statistical

efficiency• Provides data to represent

and analyze subgroups• Enables use of different

methods in strata

Disadvantages• Increased error will result

if subgroups are selected at different rates

• Especially expensive if strata on population must be created

• High cost

Selecting a Stratified Sample

219 female students (60%)

146 male students(40%)

In a population of 365 twelfth-grade American governments

66 female students(60%)

From these, she randomly selects a stratified sample of:

and 44 male students(40%)

The researcher identifies two subgroups, or strata:

Cluster

Advantages• Provides an unbiased

estimate of population parameters if properly done

• Economically more efficient than simple random

• Lowest cost per sample• Easy to do without list

Disadvantages• Often lower statistical

efficiency due to subgroups being homogeneous rather than heterogeneous

• Moderate cost

Random Sampling (Continued 3)

3. Cluster Sampling --- A randomized sample of group within the total population rather than

individuals --- Everyone within the group will need to become part of the sample --- Used when individuals are difficult to randomize --- Can only make generalizations about the group and not the individuals within the group. This is a common error researchers make.4. Two Stage Random Sampling --- Combines cluster sampling with individual sampling --- This would help to eliminate any problems with just cluster sampling --- One would randomly select a cluster and than randomly select individuals within the cluster

Stratified and Cluster SamplingStratified• Population divided into

few subgroups• Homogeneity within

subgroups• Heterogeneity between

subgroups• Choice of elements from

within each subgroup

Cluster• Population divided

into many subgroups• Heterogeneity within

subgroups• Homogeneity

between subgroups• Random choice of

subgroups

Nonprobability Samples

Cost

Feasibility

Time

No need to generalize

Limited objectives

Nonprobability Sampling Methods

Convenience

Judgment

Quota

Snowball

Random Sampling (Continued 4)• Non-Random Sampling

1. System Sampling --- Every individual in the population list is included in the sample --- Random Start-Draw a number from a hat and select every individual who is within that sampling interval2. Convenience Sampling --- A group of individuals are available for the study --- Such samples cannot be considered representative --- Demographics and characteristics need to be discussed --- To improve validity of the sample more than one study should be used to overcome an one time occurace

Random Sampling (Continued 5)

3. Purposeful Sampling --- Researcher uses personal judgment about the sample based on the knowledge of the population and the specific purpose of the research

• Generalizing from a sample1. Population generalizability --- The degree to which a sample represents the population

of interest and can be generalizable to a large group --- A representative sample is the extent to which a sample

is identical in all characteristics to the intended population

Random Sampling (Continued 6)

---- Any researcher who loses over 10 percent of the original ample would be well advised to acknowledge this limitation and qualify their conclusions accordingly ---- If the sample is less than expected the researcher should describe the sample as thoroughly as possible so that interested persons can judge for themselves to which any of the findings apply

• Ecological Generalizability ---- The degree to which the results of a study can be

extended to other settings or conditions --- The environment and or the settings have to be the same