Chapter 015

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Chapter 15

Sampling

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Sampling

Media announcements drive understanding of sampling: A random sample showed . . . Two of three people chose . . . Preferred two to one over . . .

However, in most instances, television, newspapers, and advertisements do not explain their sampling techniques

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Sampling Theory

Sampling is selecting A sampling plan is the researcher’s plan for selection A sample includes all elements included in a study The way a sample is chosen determines how it can

be generalized Developed to determine mathematically how to

acquire a representative sample Derived from survey research first used for census

reporting

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Key Concepts of Sampling Theory

Populations and elements Sampling or eligibility criteria Sample representativeness Sampling error Randomization Sampling frames Sampling plans

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Populations and Elements

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Populations and Elements (Cont’d)

Population Characteristic Target population Elements Accessible population Subject, participant, informant Generalizing Hypothetical population

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Sampling or Eligibility Criteria

Sampling criteria Inclusion criteria Exclusion criteria Twiss study If federally funded, study should be inclusive

of underrepresented groups Should not use them exclusively, especially when

risk is present Should not exclude them totally, especially when

benefit is likely

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Sample Representativeness

As much like the target population as possible In relation to the variables under consideration In relation to possibly extraneous variables

Accessible population must be representative of the target population

Population parameter Precision

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Sampling Error

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Sampling Error (Cont’d)

Any sample’s mean is expected to be slightly different from the population mean

Occurs because of variation—random or systematic The difference between a sample statistic and the

true population parameter The larger the sampling error, the less representative

the sample is of the target population The larger the sample, the more representative the

sample is of the target population Sampling error decreases power

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Random Variation

Expected, normal difference from the mean in individual values

The values are randomly scattered around the mean

As sample size increases, sample mean is more likely to have a value similar to that of the population mean

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Systematic Variation/Systematic Bias

All the values in the sample may tend to be higher or lower than the population’s mean

Occurs when the sample is NOT representative of the whole population

Nonrandom samples have more systematic bias

Exclusion criteria and high refusal rates increase systematic bias

Issues with generalizability

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Refusal Rate and Attrition

Typically, refusal to participate eliminates people with LESS of something

Sample attrition—withdrawal or loss of subjects from a study

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Randomization

Random selection—everyone in the accessible population has an equal chance of being selected

Random assignment—the sample is randomly assigned to experimental (treatment) group or control group

If subjects are not randomly assigned to a group, the group not receiving the treatment is called a comparison (not control) group

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Sampling Frame and Sampling Plan

Sampling frame—list of all members in the accessible population asked to participate

Sampling plan—strategies for obtaining the sample probability (random) nonprobability (nonrandom) methods

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Probability (Random)Sampling Methods

Reduce sampling error Leaves the selection to chance Increases study validity Four sampling designs

Simple random sampling Stratified random sampling Cluster sampling Systematic sampling

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Simple Random Sampling

Most basic Elements are selected at random from the

sampling frame With replacement Without replacement

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Stratified Random Sampling

Used when researcher wants to include certain population variables so that none is unrepresented

Disproportionate sampling Proportionate sampling

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Cluster Sampling

Samples according to natural clusters Useful when

The population is heterogeneous Elements of population cannot easily be identified

One stage or multistage Provides a means for obtaining a larger

sample at a lower cost Some disadvantages

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Systematic Sampling

Useful when a master (unordered) list exists Involves selecting every kth individual on the

list, using a starting point selected randomly

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Nonprobability (nonrandom) Sampling Methods

Not every element of the population need have an equal opportunity to be included

More likely to be nonrepresentative of the population

Five types used in nursing: convenience, quota, purposive or purposeful, network or snowball, and theoretical

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Convenience Sampling

Many quantitative/some qualitative research “Accidental sampling” Right place, right time May or may not represent the population the

researcher is targeting Considered a weak approach—biases

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Quota Sampling

Many quantitative/some qualitative research Convenience sampling with set proportions

from certain groups (just like stratified random sampling, but NOT random)

Used to assure representation May be proportionate or disproportionate in

design

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Purposive Sampling

Certain elements purposefully selected Sometimes referred to as purposeful,

judgmental, or selective sampling Conscious selection of the persons most

likely to provide useful information Commonly used in qualitative research;

provides understanding of a specific topic Less commonly used in quantitative research

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Network (Snowball) Sampling

Used to obtain subjects difficult to identify or connect with in other ways

More common in qualitative research, but may be used in quantitative research

Sometimes called snowball sampling, and occasionally chain sampling

Takes advantage of social networks

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Theoretical Sampling

Used exclusively in qualitative research Most common in grounded theory research Allows researcher to select participants who

are able to contribute to a theory in progress

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Sample Size in Quantitative Research

Power Power analysis Factors that affect power:

Effect size Type of study Number of variables Sensitivity of the measurement methods Data analysis techniques

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Effect Size (ES)

Extent to which a phenomenon is present in a population

As ES decreases, power decreases To demonstrate a small ES, the researcher

requires a larger sample To demonstrate a large ES, the researcher can

get by with a smaller sample ESs vary according to the population being

studied

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Type of Study

Descriptive case studies—small samples Other descriptive studies and correlational—

often large samples More variables require bigger samples Quasi-experimental and experimental

Small, in past—just enough to meet power analysis needs

Some multi-site moderate samples Confirmatory and exploratory studies

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Number of Variables

As number of variables under study grows, the needed sample size may also increase

If variables are highly correlated with dependent variable, effect size will increase, and sample size can be reduced

Variables included in data analysis must be carefully selected

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Measurement Sensitivity

Precision Range of measured values influences power

ESs vary according to how near the value is to the population mean

The wider the range of values sampled, the larger the ES

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Data Analysis Techniques

Differ by test Parametric tests more sensitive than non-

parametric tests, when used appropriately For t-test and ANOVA, power is increased

with equal group sizes For unequal groups, total sample size must

be larger Chi-square (2) test—weakest

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Sample Size in Qualitative Research

Focus is on the quality of information obtained

Saturation of data Scope of the study Nature of the topic Quality of the data Study design

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Research Settings

Natural setting (or field setting) Partially controlled setting Highly controlled setting (laboratory)

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Recruiting and Retaining Research Participants

Recruitment strategies differ, depending on type of study and setting

Often wise to over-enroll, to compensate for attrition

May offer a small remuneration Retention rates absolutely impossible to

predict