Educational Research

Chapter 5Selecting a Sample

Gay, Mills, and Airasian10th Edition

Topics Discussed in this Chapter

Quantitative sampling Selecting random samples Selecting non-random samples

Qualitative sampling Selecting purposive samples

Quantitative Sampling

Purpose – to identify participants from whom to seek some information

Issues Nature of the sample Size of the sample Method of selecting the sample

Quantitative Sampling Terminology

Population: all members of a specified group Target population – the population to which the

researcher ideally wants to generalize Accessible population – the population to which

the researcher has access Sample: a subset of a population Subject: a specific individual participating in

a study Sampling technique: the specific method

used to select a sample from a population

Quantitative Sampling Important issues

Representation – the extent to which the sample is representative of the population

Demographic characteristics Personal characteristics Specific traits

Generalization – the extent to which the results of the study can be reasonably extended from the sample to the population

Quantitative Sampling Important issues (continued)

Sampling error The chance occurrence that a randomly

selected sample is not representative of the population due to errors inherent in the sampling technique

Random nature of errors Controlled by selecting large samples

Quantitative Sampling Important issues (continued)

Sampling bias Some aspect of the researcher’s sampling design

creates bias in the data Non-random nature of errors Controlled by being aware of sources of sampling

bias and avoiding them Examples

Surveying only students who attend additional help sessions in a class

Using data returned from only 25% of those sent a questionnaire

Quantitative Sampling

Important issues (continued) Three fundamental steps

Identify a population Define the sample size Select the sample

Quantitative Sampling

Important issues (continued) General rules for sample size

As many subjects as possible At least thirty (30) subjects per group for

correlational, causal-comparative, and true experimental designs

At least ten (10) to twenty (20) percent of the population for descriptive designs

Quantitative Sampling

Important issues (continued) General rules for sample size (continued)

See Table 4.2 (see NEXT SLIDE) for additional guidelines for survey research

The larger the population size, the smaller the percentage of the population needed to get a representative sample

For population of less than 100, use the entire population

If the population is about 500, sample 50% If the population is about 1,500, sample 20% If the population is larger than 5,000, sample 400

Qualitative Sampling

Selecting Random Samples Known as probability sampling:

Everyone has probability of getting chosen

Best method to achieve a representative sample

Four techniques Random Stratified random Cluster Systematic

Selecting Random Samples Random sampling

Selecting subjects so that all members of a population have an equal and independent chance of being selected

Advantages Easy to conduct High probability of achieving a representative sample Meets assumptions of many statistical procedures

Disadvantages Identification of all members of the population can be

difficult Contacting all members of the sample can be difficult

Selecting Random Samples Random sampling (continued)

Selection process Identify and define the population Determine the desired sample size List all members of the population Assign all members on the list a consecutive number Select an arbitrary starting point from a table of

random numbers and read the appropriate number of digits

If the number corresponds to a number assigned to an individual in the population, that individual is in the sample; if not, ignore the number

Continue until the desired number of subjects have been selected

Selecting Random Samples Random sampling (continued)

Selection issues Use a table of random numbers (page 562)

Need to list all members of the population Ignore duplicates and numbers out of range when

sampled Potentially time consuming and frustrating

Use SPSS-Windows or other software to select a random sample

Create a SPSS-Windows data set of the population or their identification numbers

Pull-down commands Data, select cases, random sample, approximate

or exact

Selecting Random Samples Stratified random sampling

Selecting subjects so that relevant subgroups in the population (i.e., strata) are guaranteed representation

A strata represents a variable on which the researcher would like to see representation in the sample

Gender Ethnicity Grade level

Selecting Random Samples Stratified random sampling (continued)

Proportional and non-proportional (i.e., equal size)

Proportional – same proportion of subgroups in the sample as in the population

If a population has 45% females and 55% males, the sample should have 45% females and 55% males

Non-proportional – different, often equal, proportions of subgroups

Selecting the same number of children from each of the five grades in a school even though there are different numbers of children in each grade

Selecting Random Samples

Stratified random sampling (continued) Advantages

More precise sample Can be used for both proportional and non-

proportional samples Representation of subgroups in the sample

Disadvantages Identification of all members of the population

can be difficult Identifying members of all subgroups can be

difficult

Selecting Random Samples

Stratified random sampling (continued) Selection process

Identify and define the population Determine the desired sample size Identify the variable and subgroups (i.e.,

strata) for which you want to guarantee appropriate representation

Classify all members of the population as members of one of the identified subgroups

Selecting Random Samples

Stratified random sampling (continued) Selection process (continued)

For proportional stratified samples Randomly select a number of individuals from

each subgroup so the proportion of these individuals in the sample is the same as that in the population

For non-proportional stratified samples Randomly select an equal number of individuals

from each subgroup

Selecting Random Samples

Stratified random sampling (continued) Selection process for proportional samples

Identify and define the population Determine the desired sample size Identify the variable and subgroups (i.e., strata)

for which you want to guarantee appropriate representation

Classify all members of the population as members of one of the identified subgroups

Randomly select an equal number of individuals from each subgroup

Selecting Random Samples

Cluster sampling Selecting subjects by using groups that

have similar characteristics and in which subjects can be found

Clusters are locations within which an intact group of members of the population can be found

Examples Neighborhoods School districts Schools Classrooms

Selecting Random Samples

Cluster sampling (continued) Multistage sampling involves the use

of two or more sets of clusters Randomly select a number of school

districts from a population of districts Randomly select a number of schools

from within each of the school districts Randomly select a number of classrooms

from within each school

Selecting Random Samples Cluster sampling (continued)

Advantages Very useful when populations are large and

spread over a large geographic region Convenient and expedient Do not need the names of everyone in the

population Disadvantages

Representation is likely to become an issue Assumptions of some statistical procedures can

be violated (you don’t need to know which ones in this class)

Selecting Random Samples Cluster sampling (continued)

Selection process Identify and define the population Determine the desired sample size Identify and define a logical cluster List all clusters that make up the population of clusters Estimate the average number of population members

per cluster Determine the number of clusters needed by dividing

the sample size by the estimated size of a cluster Randomly select the needed numbers of clusters Include in the study all individuals in each selected

cluster

Selecting Random Samples Systematic sampling

Selecting every Kth subject from a list of the members of the population

Advantage Very easily done

Disadvantages Susceptible to systematic exclusion of some

subgroups Some members of the population don’t have an

equal chance of being included

Selecting Random Samples Systematic sampling (continued)

Selection process Identify and define the population Determine the desired sample size Obtain a list of the population Determine what K is equal to by dividing the size

of the population by the desired sample size Start at some random place in the population list Take every Kth individual on the list If the end of the list is reached before the desired

sample is reached, go back to the top of the list

Selecting Non-Random Samples

Known as non-probability sampling Use of methods that do not have

random sampling at any stage Useful when the population cannot be

described Three techniques

Convenience Purposive Quota

Selecting Non-Random Samples

Convenience sampling Selection based on the availability of

subjects Volunteers Pre-existing groups

Concerns related to representation and generalizability

Selecting Non-Random Samples Purposive sampling

Researcher believes that this is a representative sample or an appropriate sample.

Selection based on the researcher’s experience and knowledge of the individuals being sampled

Usually selected for some specific reason Knowledge and use of a particular instructional strategy Experience

Need for clear criteria for describing and defending the sample

Concerns related to representation and generalizability

Selecting Non-Random Samples Quota sampling

Selection based on the exact characteristics and quotas of subjects in the sample when it is impossible to list all members of the population

Example: “I need 35 unemployed mothers and 35 employed mothers.”

Concerns with accessibility, representation, and generalizability

Qualitative Sampling

Unique characteristics of qualitative research In-depth inquiry Immersion in the setting Importance of context Appreciation of participant’s perspectives Description of a single setting

The need for alternative sampling strategies

Qualitative Sampling Purposive techniques – relying on

the experience and insight of the researcher to select participants Intensity – compare differences of two

or more levels of the topics Students with extremely positive and

extremely negative attitudes Effective and ineffective teachers

Qualitative Sampling Purposive techniques (continued)

Homogeneous – small groups of participants who fit a narrow homogeneous topic

Criterion – all participants who meet a defined criteria

Snowball – initial participants lead to other participants

Qualitative Sampling Purposive techniques (continued)

Random purposive – given a pool of participants, random selection of a small sample

Inherent concerns related to generalizability and representation

Qualitative Sampling Sample size

Generally very small samples given the nature of the data collection methods and the data itself

Two general guidelines Redundancy of the information collected from

participants: Once you are hearing the same thing from everyone, you are done collecting that data.

Representation of the range of potential participants in the setting. Make sure that you select someone from every part of the population that you want to examine.

More subjects does not mean “better.” More than 20 is often unusual.

Generalizability

Probability sampling Begins with a

population and selects a sample from it

Generalizability to the population is relatively easy

Non-probability and purposive sampling

Begins with a sample that is NOT selected from some larger population

Must consider the population hypothetical as it is based on the characteristics of the sample

Generalizability is often very limited