chapter 9

1Copyright © 2011 by Saunders, an imprint of Elsevier Inc.

Chapter 9

Examining Populations and Samples in Research


Sampling Concepts

Sampling: Selecting a group of people, events, behaviors, or other elements with which to conduct a study

Sampling plan: Sampling method; defines the selection process

Sample: Defines the selected group of people or elements from which data are collected for a study

Members of the sample can be called the subjects or participants.


Populations and Elements

Population: A particular group of individuals or elements who are the focus of the research

Target population: An entire set of individuals or elements who meet the sampling criteria

Accessible population: The portion of the target population to which the researcher has reasonable access

Elements: Individual units of the population and sample


Generalization

Extending the findings from the sample under study to the larger population

The extent is influenced by the quality of the study and consistency of the study’s findings.


Sampling Criteria: Inclusion

Characteristics that the subject or element must possess to be part of the target population

Examples: Between the ages of 18 and 45 Ability to speak English Admitted for gallbladder surgery Diagnosed with diabetes within past month


Sampling Criteria: Exclusion

Characteristics that can cause a person or element to be excluded from the target population

Examples: Diagnosis of mental illness Less than 18 years of age Diagnosis of cognitive dysfunction Unable to read or speak English


Defining Sampling Criteria

Homogeneous sample: As similar as possible so as to control for extraneous variables

Heterogeneous sample: Represents a broad range of values Used when a narrow focus is not desirable


Inappropriate Generalizations

Samples cannot be generalized beyond their sampling criteria.

This may lead to inappropriate generalizations: Because of language or reading ability To other types of illnesses or injuries


Representativeness

The sample, the accessible population, and the target population are alike in as many ways as possible.

Need to evaluate: Setting Characteristics of subjects (age, gender, ethnicity,

income, education) Distribution of values on variables measured in the

study


Sampling Error

Difference between the population mean and the mean of the sample

Random variation The expected difference in values that occurs

when different subjects from the same sample are examined

Difference is random because some values will be higher and others lower than the average population values


Sampling error

Population Sample

Populationmean

Samplemean

Sampling Error (cont’d)


Sampling Error (cont’d)

Systematic variation (bias) Consequence of selecting subjects whose

measurement values differ in some specific way from those of the population

These values do not vary randomly around the population mean.


Random vs. Systematic Variation in Sampling

Random variation: Expected difference in values that occurs when different subjects from same sample are examined Difference is random because some values will be

higher or lower than the mean population value. As sample size increases, random variation

decreases. Systematic variation (or systematic bias):

Consequence of selecting subjects whose measurement values differ in some way from those of the population


Refusal Rate vs. Acceptance Rate

Refusal rate: Percentage of subjects who declined to participate in the study 80 subjects approached and 4 refused 4 80 = 0.05 = 5% refusal rate

Acceptance rate: Percentage of subjects who consented to be in the study 80 subjects approached and 76 accepted 76 80 = 0.95 = 95% acceptance rate


Sample Attrition and Retention

Sample attrition: Withdrawal or loss of subjects from a study Attrition rate = number of subjects withdrawing ÷

number of study subjects × 100 Sample retention: Number of subjects who

remain in and complete a study.


Random Sampling

Increases the representativeness of the sample based on the target population

Control group: Used in studies with random sampling

Comparison group: Not randomly determined


Sampling Frame and Sampling Plan

Sampling frame: A listing of every member of the population, using the sampling criteria to define membership in the population

Subjects are selected from the sampling frame

Sampling plan: Outlines strategies used to obtain a sample for a study Probability sampling plans Nonprobability sampling plans


Types of Probability Sampling

Simple random sampling Stratified random sampling Cluster sampling Systematic sampling


Simple Random Sampling

Randomly choosing the sample Can use a table of random numbers Can draw names out of a hat


Stratified Random Sampling

Ensures all levels of identified variables are adequately represented in the sample

Needs a large population with which to start Variables often stratified

Age, gender, socioeconomic status Types of nurses, sites of care


Cluster Sampling

All areas with the elements of the identified population are linked.

A randomized sample of these areas is then chosen.

Used to get a geographically diverse sample Also used when developing a sampling frame

is difficult because of a lack of knowledge of the variables


Systematic Sampling

Selecting every kth individual on the list, starting randomly

Researcher must know number of elements in the population and the sample size desired


Nonprobability Sampling

Quantitative research Convenience (accidental) sampling Quota sampling


Convenience Sampling

Also called accidental sampling Weak approach to sampling because it is

hard to control for bias The sample includes whomever is available

and willing to give consent. Representativeness is a concern.


Quota Sampling

Uses convenience sampling, but with a strategy to ensure inclusion of subject types who are likely to be underrepresented in the convenience sample

Goal is to replicate the proportions of subgroups present in the population

Works better than convenience sampling to reduce bias


Sample Size in Quantitative Studies

Affect size Type of quantitative study conducted Number of variables Measurement sensitivity Data analysis techniques


Power Analysis

Ability to detect differences in the population or capacity to correctly reject a null hypothesis

Standard power of 0.8 Level of significance

Alpha = 0.05, 0.01, 0.001


Effect Size

The effect is the presence of the phenomenon being studied.

The effect size is the extent to which the null hypothesis is false.

When the effect size is large (large variation between groups), only a small sample is needed.

Increasing the sample size increases the effect size.


Number of Variables

As the number of variables increases, the sample size may increase.

The inclusion of multiple dependent variables also increases the sample size needed.


Measurement Sensitivity

Was the tool used a reliable and valid measure of the variable?

As the variance in the instrument scores increases, the sample size needed to obtain significance increases.


Data Analysis Techniques

ANOVA and t-test require equal group sizes, which will increase power because the effect size is maximized.

Chi-square is the weakest of the tests and requires a large sample size to achieve acceptable levels of power. As the number of categories increases, the

sample size must increase as well.


Critiquing the Sample

Identify Elements Accessible population Target population

Evaluate Appropriateness of generalization in quantitative

studies


Critiquing the Sample (cont’d)

Identify the sample criteria. Judge appropriateness of the sampling

criteria. Identify the sampling method.


Nonprobability Sampling

Qualitative research Purposive sampling Network or snowball sampling Theoretical sampling


Purposeful or Purposive Sampling

Also called judgmental or selective sampling Efforts are made to include typical or atypical

subjects. Sampling is based on the researcher’s

judgment.


Network Sampling

Also called snowball sampling Takes advantage of social networks to get

the sample One person in the sample asks another to

join the sample, and so on.


Theoretical Sampling

Used in grounded theory research Data are gathered from any individual or

group that can provide relevant data for theory generation.

The sample is saturated when the data collection is complete based on the researchers’ expectations.

Diversity in the sample is encouraged.


Sample Size in Qualitative Research

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


Scope of the Study

Broad studies require larger samples than narrow studies.

The sample size must be adequate for the scope.


Nature of the Topic

If the study topic is clear, fewer subjects are needed.

If the topic is difficult to define, then a larger sample is needed.


Quality of the Data

How rich are the data? Were data collected from the best sources?


Study Design

How many interviews were carried out? Was the design adequate for the variables?


Adequacy of the Sample in Qualitative Studies

Are the sampling inclusion and exclusion criteria appropriate?

Is the sampling plan adequate to address the purpose of the study?

Is the sample size adequate? What are the refusal and mortality rates?


Adequacy of the Sample in Qualitative Studies (cont’d)

Are sample characteristics and quality described?

Is there saturation of the data? Is the setting defined?


Research Settings

Natural or field setting: uncontrolled in real life Seen in descriptive or correlational studies

Partially controlled setting: manipulated or modified by the researcher Seen in correlational, quasi-, or experimental

studies Highly controlled setting: artificially

constructed by researcher (i.e., lab setting) Seen in experimental studies

chapter 9

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