SAMPLING IN MIXED
RESEARCH
sampling
in mixed research builds on your knowledge of sampling in quantitative and qualitative research. Typically, the researcher
will select the quantitative sample using one of the quantitative
sampling techniques and the qualitative sample using one of the qualitative
sampling techniques.
Sampling in mixed research can be classified into
“mixed sampling designs.” Mixed sampling designs are classified according to
two major criteria:
The first criterion is called time orientation. Time orientation is provided by the answer
to this question: “Do the quantitative and qualitative phases occur
concurrently or sequentially?”
In a concurrent time
orientation, the data are collected for the quantitative and qualitative phases
of the study at approximately the same time.
Both sets of
data are interpreted during data analysis and interpretation. oIn a sequentialtime orientation, the data
obtained in stages; the data from the first stage are used to shape selection
of data in the second stage.
Mixed methods sampling requires an understanding and
acknowledge of the sampling strategies that occur in QUAN AND QUAL research.
Probability sampling techniques are used most often QUAN research to obtain a
sample that most accurately represent the entire population .Although
convenience sampling is sometimes used QUAL AND QUAN research. It includes
samples that are most available to the researcher. This way not be
representative of the population being studied and may yield biased data.
Because techniques for mixed methods include choosing participants for a study
using both probability and purposive sampling, a comparison of purposive and
probability sampling.
Definition
Where a sample plan envisages the
use of two or more basic methods of sampling it is termed mixed sampling. For
example, in a multistage sample, if the sampling units at one stage are drawn at random and those at another by a
systematic method, the whole process
is “mixed”.
Gole qualitative
·
Based on concept of
sampling and meaning
·
Individual perspective
(ip)is context –specific and is based
experentially,not fixed
·
Relation ship bet ween
researcher and participent can shaped
collected data
·
Socio cultural standards (culture and community) shape the
IP
Information rich data achieving saturation
Types of sampling in mixed research
In this situation, the mixed method researcher can select one of five
random (i.e., probability) sampling schemes at one or more stages of the
research process.
Simple
random sampling.
Cluster
random sampling
Stratified
random sampling.
Systematic
random sampling.
Multi-stage random samplin g
Simple random sampling.
Simple random sampling (also
referred to sampling random sampling) is
the purest and the most straightforward probability sampling strategy.
It is also the most popular method for choosing a sample among population for a
wide range of purposes. In simple random sampling each member of population is
equally likely to be chosen as part of the sample. It has been stated that “the
logic behind simple random sampling is that it removes bias from the selection
procedure and should result in representative sampling
stratisfied random sampling.it is a probability
sampling method and a form of random sampling in which the
population is divided into two or more groups (strata) according to one or more
common attributes.
Stratified random sampling intends to guarantee that
the sample represents specific sub-groups or strata. Accordingly, application
of stratified sampling method involves dividing population into different
subgroups (strata) and selecting subjects from each strata in a proportionate
manner. The table below illustrates simplistic example where sample group of 10
respondents are selected by dividing population into male and female strata in
order to achieve equal representation of both genders in the sample group.
stratified determining sample size in each stratum
in a proportionate manner to the entire population.sampling can be divided into
the following two groups: proportionate and disproportionate. Application of proportionate
stratified random sampling technique involves
In disproportionate
stratified random sampling, on the contrary, numbers of subjects recruited from each stratum does
not have to be proportionate to the total size of the population. Accordingly,
application of proportionate stratified random sampling generates more accurate
primary data compared to disproportionate sampling.
Application of Stratified Sampling: an
ExampleSuppose, you dissertation aims to
explore the leadership styles exercised by medium-level managers at Bayerische
Motoren Werke Aktiengesellschaft (BMW AG). You have selected semi-structured
in-depth interviews with managers as the most appropriate primary data collection
method to achieve the research objectives.
Application of stratified random sampling contains
the following three stages.
1. Identification of relevant stratums and
ensuring their actual representation in the population. Apart from gender as
illustrated in example above, range of criteria that can be used to divide population into different
strata include age, the level of education, status, nationality, religion and
others. Specific patterns of categorization into different stratums depends
aims and objectives of the study.
Automotive
|
Motorcycles
|
Financial services
|
Other entities
|
|||||||||
N
|
Manager
|
ü
|
N
|
Manager
|
ü
|
N
|
Manager
|
ü
|
N
|
Manager
|
ü
|
|
001
|
Hudson
|
001
|
Conrad
|
ü
|
001
|
Guzman
|
001
|
Sparks
|
||||
002
|
Bass
|
ü
|
002
|
Braun
|
002
|
Craig
|
002
|
Atkinson
|
ü
|
|||
003
|
Richmond
|
003
|
Gentry
|
003
|
Green
|
ü
|
003
|
Montes
|
||||
004
|
Tucker
|
004
|
Hartman
|
ü
|
004
|
Ballard
|
ü
|
004
|
Mcguire
|
|||
005
|
Chavez
|
ü
|
005
|
Levine
|
005
|
Cox
|
005
|
Spencer
|
ü
|
|||
006
|
Riddle
|
006
|
Griffin
|
ü
|
006
|
Dunlap
|
ü
|
006
|
Davies
|
|||
007
|
Mckinney
|
007
|
Valentine
|
007
|
Patrick
|
007
|
Bradford
|
ü
|
||||
008
|
Terrell
|
ü
|
008
|
Mcdonald
|
008
|
Gardner
|
ü
|
008
|
Collins
|
|||
009
|
Hayes
|
009
|
Brown
|
ü
|
009
|
Carpenter
|
009
|
Chen
|
||||
010
|
Escobar
|
ü
|
010
|
Kaufman
|
010
|
Vasquez
|
010
|
Hess
|
ü
|
|||
Advantages of Stratified
Sampling
|
||||||||||||
1.
Stratified
random sampling is superior to simple random sampling because the process of stratifying reduces
sampling error and ensures a greater level of representation.
|
||||||||||||
2.
Thanks
to the choice of stratified random sampling adequate representation of all subgroups
can be ensured.
|
||||||||||||
3.
When
there is homogeneity within strata and heterogeneity between strata, the
estimates can be as precise (or even more precise) as with the use of simple
random sampling.
|
||||||||||||
Disadvantages of
Stratified Sampling
|
||||||||||||
1.
The
application of stratified random sampling requires the knowledge of strata
membership a priori. The requirement to be able to easily distinguish between
strata in the sample frame may create difficulties in practical levels.
|
||||||||||||
2.
Research
process may take longer and prove to be more expensive due to the extra stage
in the sampling procedure.
|
||||||||||||
3.
The
choice of stratified sampling method adds certain complexity to the analysis
plan.
|
||||||||||||
In our
case, BMW Group employees are employed across four business segments –
automotive, motorcycles, financial services and other entities. Accordingly,
each segment can be adapted as stratum to draw sample group members.
2. Numbering each subject within
each stratum with a unique identification number.
3. Selection of sufficient
numbers of subjects from each stratum.
It is critically important for samples from each stratum to be selected in a
random manner so that the relevance of bias can be minimized.
As it is illustrated in the table below, following the
procedure described above results in the sample group of 16 respondents, BMW
Group medium level managers that proportionately represent all four business
segments of the company.
Cluster Sampling.Cluster sampling (also known as one-stage cluster
sampling) is a technique in which clusters of participants that represent the
population are identified and included in the sample. Cluster sampling
involves identification of cluster of participants representing the population
and their inclusion in the sample group. This is a popular method in conducting
marketing researches.
The main aim of cluster
sampling can be specified as cost reduction and increasing the levels of
efficiency of sampling. This specific technique can also be applied in
integration with multi-stage
sampling.
A major difference between
cluster and stratified
sampling relates to the fact that
in cluster sampling a cluster is perceived as a sampling unit, whereas in
stratified sampling only specific elements of strata are accepted as sampling
unit.
Accordingly, in cluster sampling a complete list of
clusters represent the sampling frame. Then, a few clusters are chosen randomly
as the source of primary data.
Area or geographical sampling can be specified as the
most popular version of cluster sampling. Specifically, a specific area can be
divided into clusters and primary data can be collected from each cluster to
represent the viewpoint of the whole area.
The pattern of cluster analysis depends on comparative
size of separate clusters. If there are no major differences between sizes of
clusters, then analysis can be facilitated by combining clusters.
Alternatively, if there are vast differences in sizes of clusters probability
proportionate to sample size can be applied to conduct the analysis.
Application of Cluster Sampling: an Example
Imagine you want to evaluate consumer spending on various
modes of transportation in Greater London. Since Greater London is a large
area, we need to sample from only 6 boroughs out of total 32 boroughs it
comprises.
There are three stages for the application of cluster
sampling:
1. Select a
cluster grouping as a sampling frame. In example above, all 32 boroughs of the
Greater London represent the sampling frame for the study
2. Mark each
cluster with a unique number. We can easily number each borough from 1 to 32.
3. Choose a
sample of clusters applying probability sampling. Usingsystematic random sampling (or
any other probability sampling), we can choose 6 boroughs from the total
32 boroughs. Households residing in 6 boroughs will represent samples for the
study.
Advantages of Cluster Sampling
1. It is the most time-efficient and
cost-efficient probability design for large geographical areas
2. This method is easy to be used from
practicality viewpoint
3. Larger sample size can be used due to
increased level of accessibility of perspective sample group members
Disadvantages of Cluster Sampling
1. Requires group-level information to be
known
2. Commonly has higher sampling error than
othersampling techniques
3. Cluster sampling may fail to reflect the
diversity in the sampling frame
Accordingly, in cluster
sampling a complete list of clusters represent the sampling frame. Then, a few
clusters are chosen randomly as the source of primary data. sampling involves
identification of cluster of participants representing the population and their
inclusion in the sample group.
Area or geographical sampling can be specified as the
most popular version of cluster sampling. Specifically, a specific area can be
divided into clusters and primary data can be collected from each cluster to
represent the viewpoint of the whole area.
The pattern of cluster analysis depends on comparative
size of separate clusters. If there are no major differences between sizes of
clusters, then analysis can be facilitated by combining clusters.
Alternatively, if there are vast differences in sizes of clusters probability
proportionate to sample size can be applied to conduct the analysis.
Application of Cluster
Sampling: an Example
Imagine you want to evaluate consumer spending on various
modes of transportation in Greater London. Since Greater London is a large
area, we need to sample from only 6 boroughs out of total 32 boroughs it
comprises.
There are three stages for the application of cluster
sampling:
1.
Select a cluster grouping
as a sampling frame. In example above, all 32 boroughs of the Greater London represent the
sampling frame for the study
2. Mark each
cluster with a unique number. We can easily number each borough from 1 to 32.
3. Choose a
sample of clusters applying probability sampling. Usingsystematic random sampling (or any
other probability sampling), we can choose 6 boroughs from the total 32
boroughs. Households residing in 6 boroughs will represent samples for the study.
Advantages of Cluster Sampling
1. It is the most time-efficient and
cost-efficient probability design for large geographical areas
2. This method is easy to be used from
practicality viewpoint
3. Larger sample size can be used due to
increased level of accessibility of perspective sample group members
Disadvantages of Cluster
Sampling
1.
Requires
group-level information to be known
2.
Commonly
has higher sampling error than othersampling techniques
3.
Cluster
sampling may fail to reflect the diversity in the sampling frame
multiti-stage sampling.Multi-stage sampling (also known as multi-stage cluster sampling) is a
more complex form of cluster sampling which contains two or more stages
in sample selection. In simple terms, in multi-stage sampling large clusters of
population are divided into smaller clusters in several stages in order to make
primary data collection more manageable. It has to be acknowledged that
multi-stage sampling is not as effective as true random sampling; however, it
addresses certain disadvantages associated with true random sampling such as
being small sample of relevant
discrete groups.
1. Choosing a sampling frame of relevant
discrete sub-groups. This should be done from relevant discrete groups selected
in the previous stage.
2. Repeat the second stage above, if necessary.
overly
expensive and time-consuming.
Application of
Multi-Stage Sampling: an Example
Contrary to its name,
multi-stage sampling can be easy to apply in business studies. Application of
this sampling method can be divided into four stages:
3.
Choosing sampling frame, numbering each group with a
unique number and selecting a
4.
Choosing the members of the sample group from the
sub-groups using some variation of probability sampling.
Let’s illustrate the
application of the stages above using a specific example.
Your research objective is
to evaluate online spending patterns of households in the US through online
questionnaires. You can form your sample group comprising 120 households in the
following manner:
1. sampling methods. This will result in 120
households to be included in your sample group. Choose 6 states in the USA
using simple random sampling (or any other probability sampling).
2. Choose 4 districts within each state using a systematic sampling method (or any other probability sampling).
3. Choose 5 households
from each district using simple random or systematic
Advantages of
Multi-Stage Sampling
1. Effective in primary data collection from
geographically dispersed. the population when face-to-face contact in required
(e.g. semi-structured in-depth interviews)
2. Cost-effectiveness and time-effectiveness.
3. High level of flexibility.
Disadvantages of Multi-Stage Sampling
1.
High
level of subjectivity.
2.
Research
findings can never be 100% representative of population The presence of
group-levelinformatiorequi
systematic sampling.In system systematic sampling (also called systematic random
sampling) every Nth member of the population is selected to be included in the
study. It is a probability
sampling method. It has been
stated that “with systematic sampling, every Kth item is selected to produce a
sample of size n from a population size of N”.Systematic sampling requires an
approximated frame for a priori but not the full
As it is the case with any other sampling method, you
will have to obtain confirmation from your dissertation supervisor about your
choice of systematic sampling, the total size of the population, size of your sample
group and the value of N sample fraction before starting collecting the primary
data.
Application of Systematic Sampling: an Example
You can
apply systematic sampling in your thesis in the following manner:
1. Label each member of the sample
group with a unique identification number (ID).
2. Calculate the sampling fraction by dividing the sample size to the
total number of the population:
The sampling fraction result is a guidance for applying
systematic sampling. For example, if your sampling fraction is equal to 1/5,
you will need to choose one in every five cases; that is every fifth case from
the sampling frame. In instances where calculations result in a more
complicated fraction, especially for large sample sizes, you can round your
population to the nearest 10 or 100.
3. The first sample has
to be chosen in a random manner. It
is important to select the first sample randomly to ensure probability sampling
aspect of the systematic sampling. In other words, if the first sample is
selected from the start of the sample frame all the time, the samples between
the sample fractions (samples between every fifth cases in example above) will
not have a chance of being included in the sample group. Therefore, the fist
case needs to be selected randomly to overcome this issue.
4. Additional members of
sample group are chosen by recruiting each Nth subject(5th subject in
example above) among the population.
Let’s illustrate the application of stages above using a
specific example.
Suppose your dissertation topic is A Study into the
Impact Leadership Style on Employee Motivation in ABC Company and you have
chosen semi-structured in-depth interview as primary data collection method.
ABC Company has 200 operational level employees who could be potentially
interviewed. You identified your sample size as 24 subjects, i.e. you will
interview 12 employees.
You will have to do the following:
1. Label each employee
with a unique number.
2. Calculate the
sampling 15; #23fraction.
Sampling fraction = Actual Sample Size/Total Population =
24/200 = 3/25.
This sampling fraction can
be narrowed down to 1/8. Accordingly, every 8th member of
the sampling frame needs to be selected to participate in the study.
3. Choose the first
sample randomly. Suppose you
randomly seleced the sample #47 as the starting point for selecting samples. Accordingly, your sample group will
comprise of ABC Company employees under the following numbers: #47; #55;
#63; #71; #79; #87; #95; #103; #111; #119; #127; #135; #143; #151; #159; #167;
#175; #183; #191; #199; #7; #; #31.
Advantages of Systematic Sampling
1. When done correctly, this method will
approximate the results of simple random sampling.
2. Systematic sampling is cost and time
efficient. This is an important aspect of systematic sampling which makes it
applicable in many situations.
3. Systematic sampling is effectively suitable
in collecting data from geographically disperse cases (that do not require
face-to-face contact).
Disadvantages of Systematic Sampling
1. Systematic sampling can be applied only if
the complete list of the population is available.
2. If there are periodic patterns within the
dataset, the sample will be biased.
3.
If
study participants deduce the sampling interval, this can bias the population
as non-participants will be different from study
participants.
Role Of Sampling In Mixed Research
The purpose of this article is to emphasize the importance of sampling in
all mixed methods research studies. Effective meaning-making in mixed methods
research studies is very much dependent on the quality of inferences that
emerge, which, in turn, is dependent on the quality of the underlying sampling
design. Further, these inferences are only of a quality nature if interpretive
consistency occurs, which represents the justifiableness of the type of
generalization made, given the sampling design. In earlier work, we
identified six sampling-based considerations that all mixed methods researchers
should make at the four broad stages (i. e., research conceptualization,
research planning, research implementation, and research dissemination stages)
of the mixed methods research process: emtic orientation, probabilistic
orientation, abductive orientation, intrinsic versus instrumental orientation,
particularistic versus universalistic orientation, and philosophical clarity.
Building on this six-element framework, we outline how focusing on sampling
considerations at the four stages of the mixed methods research process,
which includes the dissemination stage of reporting the mixed methods research
findings to stakeholders enhance significantly the process of meaning-making.
We believe that addressing these sampling considerations at each of these
stages will increase the likelihood that the mixed methods researcher will
uphold interpretive consistency.
Table 2.
Advantages and Disadvantages
Advantage
|
Disadvantage
|
The analysis of quantitative data and qualitative
The collection of both open and
closed-ended data in response to the research question.
|
It takes much more time and resources to
plan and this type of research this time-consuming activity.
|
Planning and implementing method one beyond drawing on the finding of another always prove to be difficult.
|
‘
Reference
d‘Definition of Sampling in Mixed Research - Google Search’. Accessed
20 August 2019.
https://www.google.com/search?q=definition+of+sampling+in+mixed+research&oq=de&aqs=chrome.0.69i59j69i57j69i59j0l2j69i60.9388j0j8&sourceid=chrome&ie=UTF-8.
Onwuegbuzie, Anthony J., and Kathleen
M. T. Collins. ‘The Role of Sampling in Mixed Methods-Research’. KZfSS
Kölner Zeitschrift Für Soziologie Und Sozialpsychologie 69, no. 2 (1
October 2017): 133–56. https://doi.org/10.1007/s11577-017-0455-0.
https://docs.google.com/document/d/e/2PACX-1vTIbVaubmA5fGh2i-Valrz9Yjbwo_7WMrwRyfuXcpqIpm44pqSeaXT-IBix1sjknw/pub
Screen reader support enabled.
https://docs.google.com/forms/d/e/1FAIpQLSftPiZ5jmtAn1ELVVWi49aCPo4ZDyP2_ZDzGkSlyO3TT6YZ8Q/viewform?usp=sf_link
Comments
Post a Comment