Qualitative Research Sampling 2

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Educational Research

Topic 4


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Topics Discussed in this Chapter

Quantitative sampling

Selecting random samples

Selecting non-random samples

Qualitative sampling

Selecting purposive samples


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Quantitative Sampling

Purposeto identify participants from

whom to seek some information Issues

Nature of the sample

Size of the sample Method of selecting the sample


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Quantitative Sampling Terminology

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

researcher ideally wants to generalize

Accessible populationthe population to which theresearcher has access

Sample: a subset of a population

Subject: a specific individual participating in astudy

Sampling technique: the specific method used toselect a sample from a population

Obj. 1.1, 1.2, & 1.3


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Quantitative Sampling Important issues

Representationthe extent to which the sample

is representative of the population Demographic characteristics

Personal characteristics

Specific traits

Generalizationthe extent to which the results ofthe study can be reasonably extended from thesample to the population

Obj. 1.4


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Quantitative Sampling Important issues (continued)

Sampling error

The chance occurrence that a randomlyselected sample is not representative of thepopulation due to errors inherent in thesampling technique

Random nature of errors Controlled by selecting large samples

Obj. 6.1


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Sampling bias Some aspect of the researchers sampling design creates

bias in the data

Non-random nature of errors

Controlled by being aware of sources of sampling biasand avoiding them

Examples

Surveying only students who attend additional helpsessions in a class

Using data returned from only 25% of those sent aquestionnaire

Obj. 6.2


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Important issues (continued)

Three fundamental steps

Identify a population

Define the sample size

Select the sample

Obj. 1.5


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Important issues (continued)

General rules for sample sizeAs many subjects as possible

Thirty (30) subjects per group for correlational,causal-comparative, and true experimental

designs Ten (10) to twenty (20) percent of the

population for descriptive designs

Obj. 1.8


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General rules for sample size (continued)

See Table 4.2 for additional guidelines for surveyresearch

The larger the population size, the smaller the percentageof 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

Obj. 1.9


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Selecting Random Samples Known as probability sampling

Best method to achieve a

representative sample Four techniques

Random

Stratified random Cluster

Systematic

Obj. 1.7


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Selecting Random Samples Random sampling (continued)

Selection issues Use a table of random numbers

Need to list allmembers 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 randomsample

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

Pull-down commands

Data, select cases, random sample, approximate orexact


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Selecting Random Samples Stratified random sampling

Selecting subjects so that relevant subgroups in

the population (i.e., strata) are guaranteedrepresentation

A strata represents a variable on which theresearcher would like to see representation in thesample

Gender

Ethnicity

Grade level

Obj. 3.1 & 3.3


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Selecting Random Samples Stratified random sampling (continued)

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

Proportionalsame proportion of subgroups in thesample as in the population

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

Non-proportionaldifferent, often equal, proportions of

subgroups Selecting the same number of children from each of the

five grades in a school even though there are differentnumbers of children in each grade

Obj. 3.4


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Selecting Random Samples

Stratified random sampling (continued)

Advantages More precise sample

Can be used for both proportional and non-proportionalsamples

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

Obj. 3.2 & 4.9


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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 guaranteeappropriate representation

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

Obj. 4.1


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Selection process (continued) For proportional stratified samples

Randomly select a number of individuals from eachsubgroup so the proportion of these individuals inthe sample is the same as that in the population

For non-proportional stratified samples

Randomly select an equal number of individuals fromeach subgroup

Obj. 4.1


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Selection process for proportional samples Identify and define the population


Identify the variable and subgroups (i.e., strata) forwhich you want to guarantee appropriate representation

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

Randomly select an equal number of individuals fromeach subgroup

Obj. 4.1


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Selecting Random Samples Cluster sampling

Selecting subjects by using groups that havesimilar characteristics and in which subjects canbe found

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

Examples Neighborhoods

School districts

Schools

Classrooms

Obj. 4.3


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Selecting Random Samples Cluster sampling (continued)

Multistage sampling involves the use oftwo or more sets of clusters

Randomly select a number of school districtsfrom a population of districts

Randomly select a number of schools fromwithin each of the school districts

Randomly select a number of classrooms fromwithin each school

Obj. 4.6


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Advantages

Very useful when populations are large and spread overa 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 beviolated

Obj. 4.9


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Identify and define a logical cluster

List all clusters that make up the population of clusters

Estimate the average number of population members percluster

Determine the number of clusters needed by dividing thesample size by the estimated size of a cluster

Randomly select the needed numbers of clusters

Include in the study all individuals in each selectedcluster

Obj. 4.4


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Selecting Random Samples Systematic sampling

Selecting every Kthsubject 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 dont have an equal

chance of being included

Obj. 4.7 & 4.9


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Selecting Random Samples Systematic sampling (continued)



Obtain a list of the population

Determine what K is equal to by dividing the size of thepopulation by the desired sample size

Start at some random place in the population list

Take every Kthindividual on the list

If the end of the list is reached before the desiredsample is reached, go back to the top of the list

Obj. 4.8


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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

Obj. 5.1


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Selecting Non-Random Samples Convenience sampling

Selection based on the availability of

subjectsVolunteers

Pre-existing groups

Concerns related to representation andgeneralizability

Obj. 5.2 & 5.3


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Selecting Non-Random Samples Quota sampling

Selection based on the exact characteristics

and quotas of subjects in the sample whenit is impossible to list all members of thepopulation

Concerns with accessibility, representation,and generalizability

Obj. 5.2 & 5.5


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Quantitative Sampling Comments Both probability and non-random sampling

techniques are used in quantitative research Probability models are desired due to the selection

of a representative sample and the ease withwhich the results can be generalized to thepopulation

Non-random (i.e., non-probability) models are

frequently used due the reality of the situations inwhich the research is being conducted Concerns with representation

Concerns with generalization


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Qualitative Sampling

Unique characteristics of qualitative research

In-depth inquiry Immersion in the setting

Importance of context

Appreciation of participants perspectives

Description of a single setting The need for alternative sampling strategies

Obj. 7.2


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Qualitative Sampling Purposive techniquesrelying on the

experience and insight of the researcher

to select participants Intensitycompare differences of two or

more levels of the topics

Students with extremely positive and extremelynegative attitudes

Effective and ineffective teachers

Obj. 7.3


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Qualitative Sampling Purposive techniques (continued)

Homogeneoussmall groups of

participants who fit a narrow homogeneoustopic

Criterionall participants who meet adefined criteria

Snowballinitial participants lead to otherparticipants

Obj. 7.4, 7.5, & 7.6


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Qualitative Sampling Purposive techniques (continued)

Random purposivegiven a pool of

participants, random selection of a smallsample

Combinations of techniques

Inherent concerns related togeneralizability and representation

Obj. 7.7 & 7.8


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Qualitative Sampling Sample size

Generally very small samples given the

nature of the data collection methods andthe data itself

Two general guidelines

Redundancy of the information collected fromparticipants

Representation of the range of potentialparticipants in the setting

Obj. 7.9


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Generalizability Probability sampling

Begins with a population

and selects a samplefrom it

Generalizability to thepopulation is relativelyeasy

Non-probability andpurposive sampling

Begins with a samplethat is NOT selected fromsome larger population

Must consider thepopulation hypothetical

as it is based on thecharacteristics of thesample

Generalizability is oftenvery limited

Obj 7 10

Qualitative Research Sampling 2

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