BUSINESS RESEARCH METHOD UNIT III
RESEARCH
DESIGN
A research design is a
“Blue Print” for collection, measurement and analysis of data. It outlines how
the research will be carried out. It is like glue which sticks together the
entire process of research. It provides answers to various questions like -
What techniques will be used to gather data. What kind of sampling will be
used? How time and cost constraints be dealt with? Etc.
Essentials of Research
Design
1. The design should be
an activity and time based plan
2. It is always based
on research question
3. It guides the
selection of sources and types of information
4. It indicates a
framework for specifying the relationship among the study’s variables
5. Outlines procedures
for every research activity
6. It must be
appropriate, efficient and economical
7. It should be
flexible
8. It must be adequate
Types of Research
Design
“You
cannot put the same shoe on every foot” - Syrus
Although every problem
and research objective may seem unique, there are usually enough similarities
among problems and objectives to allow decisions to be made in advance about
the best plan to resolve the problem. There are some basic research designs
that can be successfully matched to given problems and research objectives.
Three traditional
categories of research design:
• Exploratory
• Descriptive
• Causal
The choice of the most
appropriate design depends largely on the objectives of the research and how
much is known about the problem and these objectives. The overall research
design for a project may include one or more of these three designs as part(s)
of it.
Further, if more than
one design is to be used, typically we progress from Exploratory toward Causal.
Basic Research
Objectives and Research Design
Research Objective
Appropriate Design
To gain background
information, to define terms, to clarify exploratory problems and develop
hypotheses, to establish research priorities, to develop questions to be answered
To describe and measure
phenomena at a point Descriptive
In time
To determine causality,
test hypotheses, to make “if-then” Causal
Statements, to answer
questions
Research Design:
Exploratory Research
Exploratory research is
most commonly unstructured, “informal” research that is undertaken to gain
background information about the general nature of the research problem.
Exploratory research is usually conducted when the researcher does not know
much about the problem and needs additional information or desires new or more
recent information. Exploratory research helps diagnose the dimensions of the
problem so that successive research will be on target. It helps to set priorities
for research. Exploratory research is used in a number of situations:
• To gain background
information
• To define terms
• To clarify problems
and hypotheses
• To establish research
priorities
A variety of methods
are available to conduct exploratory research:
• Secondary Data
Analysis
• Experience Surveys
• Case Analysis
• Focus Groups
• Projective Techniques
Categories of
Exploratory Research
• Experience Surveys: -
Issues and ideas may be discussed with persons who have had
personal experience in
the field.
• Secondary data
analysis: - Another quick and economical source of background information is
existing literature containing data that has been compiled for some purpose
other than the purpose in hand
• Case Study method:
-obtains information from one or a few situations that are similar to the problem
situation. Primary advantage is that an entire organisation or entity can be investigated
in depth and with meticulous attention to detail.
• Pilot Studies are
used in different types of designs. - Within the context of exploratory research
it covers some part of the research on a small scale. Major categories of pilot
study include focus group interviews, projective techniques, and depth
interviews.
Categories of Pilot
Studies
• Focus Group
interviews: - Unstructured, free flowing, group dynamic sessions that allow individuals
the opportunity to initiate the topics of discussion. There is synergistic and spontaneous
interaction among the respondents. Found to be highly advantageous.
• Projective
techniques; - An indirect means of questioning the respondents. Uses word association
tests, sentence completion test, third person test, role playing technique and
Thematic Apperception
Test.
• Depth interviews: -
unstructured, extensive interviews that encourage an individual to talk freely
and in depth about a topic
Historical Research
History, the meaningful
record of human achievement, helps us to understand the present and to some
extent, to predict the future.
• Used to “prevent
reinventing the wheel” every few years.
• It is the application
of scientific method to the description and analysis of past events
Descriptive Research
Descriptive research is
undertaken to provide answers to questions of who, what, where, when, and how –
but not why.
Two basic
classifications:
• Cross-sectional
studies
• Longitudinal
studies
Research Design
Descriptive Research
-Cross-sectional Studies
• Cross-sectional
studies measure units from a sample of the population at only one point in time.
• Sample surveys are
cross-sectional studies whose samples are drawn in such a way as to be representative
of a specific population.
• On-line survey
research is being used to collect data for cross-sectional surveys at a faster rate
of speed.
Descriptive Research
-Longitudinal Studies
• Longitudinal studies
repeatedly draw sample units of a population over time.
• One method is to draw
different units from the same sampling frame.
• A second method is to
use a “panel” where the same people are asked to respond periodically.
• On-line survey
research firms recruit panel members to respond to online queries.
Research Design: Causal
Research
• Causality may be
thought of as understanding a phenomenon in terms of conditional statements of
the form “If x, then y.”
• Causal relationships
are typically determined by the use of experiments, but other methods are also
used.
Experiments
An experiment is
defined as manipulating (changing values/situations) one or more independent variables
to see how the dependent variable(s) is/are affected, while also controlling
the effects of additional extraneous variables.
– Independent
variables: - that over which the researcher has control and wishes to
manipulate i.e. package size, ad copy, price.
– Dependent variables:
- that over which the researcher has little to no direct control, but has a strong
interest in testing i.e. sales, profit, market share.
– Extraneous variables:
- those that may affect a dependent variable but are not independent variables.
Experimental Design
An experimental design
is a procedure for devising an experimental setting such that a change in the
dependent variable may be solely attributed to a change in an independent
variable.
Symbols of an
experimental design:
• O =
measurement of a dependent variable
• X =
manipulation, or change, of an independent variable
• R = random
assignment of subjects to experimental and control groups
• E =
experimental effect
After-Only Design: X O1
One-Group, Before-After
Design: O1 X O2
Before-After with
Control Group:
• Experimental group:
O1 X O2
• Control group: O3 O4
• Where E = (O2 – O1) –
(O4 – O3)
How Valid Are
Experiments?
An experiment is valid
if:
• The observed change
in the dependent variable is, in fact, due to the independent variable (internal
validity)
• If the results of the
experiment apply to the “real world” outside the experimental setting (external
validity)
Choosing the right
instrument for data collection
• The instrument you
choose for data collection affects your entire study.
• Validity is your
primary concern!
• Reliability is a
secondary concern
What is the Validity of
a Study?
Internal Validity –
The degree to which changes in the dependent variable are affected by the manipulated
independent variable. Maintaining high internal validity means controlling for
all other independent variables other than the one(s) being studied
External Validity –
The degree to which the results of a study can be generalized to the “real world”.
Factors that negatively affect external validity also negatively affect the
generalizability of the results
Instrument Validity
Does an instrument
measure what it is supposed to measure? Four types of instrument validity are
as follows:
– Construct
– Criterion related
– Content
– Inter-rater /
Intra-rater
Construct Validity
It is the most
important type of validity. Construct validity is the degree to which the instrument
actually measures whether or not an underlying construct is being measured.
For example, does a
math test actually measure math achievement? Does a personality test actually
measure personality?
Criterion Related Validity
Criterion Related
Validity is of two types:-
• Concurrent validity –
Degree to which scores on one test are correlated with scores on another test
administered at the same time. Only one group is used.
• Predictive validity –
Degree to which scores on one test predicts scores on a test administered in
the future. Only one group is used.
Reliability
Reliability is the
consistency with which an instrument measures the construct or content area it
is intended to measure. Reliability is established using such techniques as
• Split-half,
• Rationale equivalence
and inter-rater
Reliability is reported
as a coefficient ranging from 0.00 (low) to +1.00 (high). Anything above .70 is
considered sufficient for most cases
Measures of Reliability
• Stability (test /
re-test)
• Equivalence
(alternate forms)
• Equivalence and
Stability Combined
• Internal consistency
• Scorer / Rater
Internal Consistency
Questions on tests
should be equally difficult throughout entire instrument
Split-half – Used
with dichotomous tests
Kuder-Richardson 20 /
21 – Improvement on split-half
Cronbach’s Alpha –
Only used with instruments with more than two scores (e.g., Likert
Scales)
Sampling Design
Sampling is concerned
with the selection of a subset of individuals from within a statistical population
to estimate characteristics of the whole population. Two advantages of sampling
are that the cost is lower and data collection is faster than measuring the
entire population. A Sample design is a definite plan for obtaining a sample from
a given population
Definition
According to Gerald
Hursh “a Sample Design is the theoretical basis and the practical means by
which we infer the characteristics of some population by generalizing from the characteristics
of relatively few of the units comprising the population
Steps in Sampling
Design
1. Define the
population or universe
2. State the sampling
frame
3. Identify the
sampling unit
4. State sampling
method
5. Determine the sample
size
6. Spell out the
sampling plan
7. Select the sample
Population Definition
Successful statistical
practice is based on focused problem definition. In sampling, this includes
defining the population from which our sample is drawn. A population can be
defined as including all people or items with the characteristic one wish to
understand. Because there is very rarely enough time or money to gather
information from everyone or everything in a population, the goal becomes
finding a representative sample (or subset) of that population.
Sometimes that which
defines a population is obvious. For example, a manufacturer needs to decide
whether a batch of material from production is of high enough quality to be
released to the customer, or should be sentenced for scrap or rework due to
poor quality. In this case, the batch is the population.
Although the population
of interest often consists of physical objects, sometimes we need to sample
over time, space, or some combination of these dimensions. For instance, an
investigation of supermarket staffing could examine checkout line length at
various times, or a study on endangered penguins might aim to understand their
usage of various hunting grounds over time.
For the time dimension,
the focus may be on periods or discrete occasions.
In other cases, our
'population' may be even less tangible. For example, Joseph
Jagger studied the
behaviour of roulette wheels at a casino in Monte Carlo, and used this to
identify a biased wheel. In this case, the 'population' Jagger wanted to
investigate was the overall behaviour of the wheel (i.e. the probability
distribution of its results over infinitely many trials), while his 'sample'
was formed from observed results from that wheel. Similar considerations arise
when taking repeated measurements of some physical characteristic such as the
electrical conductivity of copper.
This situation often
arises when we seek knowledge about the cause system of which the observed population
is an outcome. In such cases, sampling theory may treat the observed population
as a sample from a larger 'super population'. For example, a researcher might
study the success rate of a new 'quit smoking' program on a test group of 100
patients, in order to predict the effects of the program if it were made
available nationwide. Here the super population is "everybody in the
country, given access to this treatment" - a group which does not yet
exists, since the program isn't yet available to all.
Note also that the
population from which the sample is drawn may not be the same as the population
about which we actually want information. Often there is large but not complete
overlap between these two groups due to frame issues etc. (see below).
Sometimes they may be entirely separate - for instance, we might study rats in
order to get a better understanding of human health, or we might study records
from people born in 2008 in order to make predictions about people born in
2009.
Time spent in making
the sampled population and population of concern precise is often well spent,
because it raises many issues, ambiguities and questions that would otherwise
have been overlooked at this stage.
Sampling Frame
In the most
straightforward case, such as the sentencing of a batch of material from production
(acceptance sampling by lots), it is possible to identify and measure every single
item in the population and to include any one of them in our sample. However,
in the more general case this is not possible. Where voting is not compulsory,
there is no way to identify which people will actually vote at a forthcoming
election (in advance of the election). These imprecise populations are not
amenable to sampling in any of the ways below and to which we could apply
statistical theory.
As a remedy, we seek a
sampling frame which has the property that we can identify every single element
and include any in our sample. The most straightforward type of frame is a list
of elements of the population (preferably the entire population) with
appropriate contact information.
For example, in an
opinion poll, possible sampling frames include an electoral register and a
telephone directory.
Problem Related With
Sampling Frame
1. Non coverage and
incompleteness.
2. Appearance of
cluster of element.
3. Inclusion of foreign
element in the list.
Probability and
Non-Probability Sampling
A probability sampling
is one in which every unit in the population has a chance (greater than zero)
of being selected in the sample, and this probability can be accurately
determined. The combination of these traits makes it possible to produce
unbiased estimates of population totals, by weighting sampled units according
to their probability of selection.
Example: We want to
estimate the total income of adults living in a given street. We visit each
household in that street, identify all adults living there, and randomly select
one adult from each household. (For example, we can allocate each person a
random number, generated from a uniform distribution between 0 and 1, and
select the person with the highest number in each household). We then interview
the selected person and find their income. People living on their own are
certain to be selected, so we simply add their income to our estimate of the
total. But a person living in a household of two adults has only a one-in-two
chance of selection. To reflect this, when we come to such a household, we
would count the selected person's income twice towards the total. (The person
who is selected from that household can be loosely viewed as also representing the
person who isn't selected.)
In the above example,
not everybody has the same probability of selection; what makes it a probability
sample is the fact that each person's probability is known. When every element
in the population does have the same probability of selection, this is
known as an 'equal probability of selection' (EPS) design. Such designs are
also referred to as 'self-weighting' because all sampled units are given the
same weight. Probability sampling includes: Simple Random Sampling, Systematic
Sampling, and Stratified Sampling, Probability Proportional to Size Sampling,
and Cluster or Multistage Sampling. These various ways of probability sampling
have two things in common:
1. Every element has a
known nonzero probability of being sampled and
2. Involves random
selection at some point.
Non Probability Sampling;
- Non
Probability Sampling is any sampling method where some elements of the
population have no chance of selection (these are sometimes referred to
as 'out of coverage'/'under covered'), or where the probability of selection
can't be accurately determined. It involves the selection of elements based on
assumptions regarding the population of interest, which forms the criteria for
selection. Hence, because the selection of elements is nonrandom, non-probability
sampling does not allow the estimation of sampling errors. These conditions
give rise to exclusion bias, placing limits on how much information a sample
can provide about the population. Information about the relationship between
sample and population is limited, making it
difficult to
extrapolate from the sample to the population.
Example: We visit every
household in a given street, and interview the first person to answer the door.
In any household with more than one occupant, this is a nonprobability sample,
because some people are more likely to answer the door (e.g. an unemployed person
who spends most of their time at home is more likely to answer than an employed
housemate who might be at work when the interviewer calls) and it's not
practical to calculate these probabilities.
Non probability
sampling methods include accidental sampling, quota sampling and purposive
sampling. In addition, no- response effects may turn any probability
design into a non probability design if the characteristics of non response are
not well understood, since non response effectively modifies each element's
probability of being sampled.
Sampling Methods
Within any of the types
of frame identified above, a variety of sampling methods can be employed,
individually or in combination. Factors commonly influencing the choice between
these designs include:
Nature and quality of
the frame
Availability of
auxiliary information about units on the frame
Accuracy
requirements, and the need to measure accuracy
Whether detailed
analysis of the sample is expected
Cost/operational
concerns
Simple Random Sampling
In a simple random
sample (SRS) of a given size, all such subsets of the frame are given an equal
probability. Each element of the frame thus has an equal probability of
selection: the frame is not subdivided or partitioned. Furthermore, any given pair
of elements has the same chance of selection as any other such pair (and
similarly for triples, and so on). This minimises bias and simplifies analysis
of results. In particular, the variance between individual results within the sample
is a good indicator of variance in the overall population, which makes it
relatively easy to estimate the accuracy of results.
However, SRS can be
vulnerable to sampling error because the randomness of the selection may result
in a sample that doesn't reflect the makeup of the population. For instance, a
simple random sample of ten people from a given country will on average produce
five men and five women, but any given trial is likely to over represent one
sex and under represent the other.
Systematic and
stratified techniques, discussed below, attempt to overcome this problem by
using information about the population to choose a more representative sample.
SRS may also be
cumbersome and tedious when sampling from an unusually large target population.
In some cases, investigators are interested in research questions specific to
subgroups of the population. For example, researchers might be interested in
examining whether cognitive ability as a predictor of job performance is
equally applicable across racial groups. SRS cannot accommodate the needs of
researchers in this situation because it does not provide subsamples of the
population. Stratified sampling, which is discussed below, addresses this weakness
of SRS.
Simple random sampling
is always an EPS design (equal probability of selection), but not all EPS
designs are simple random sampling.
Systematic Sampling
Systematic sampling
relies on arranging the study population according to some ordering scheme and
then selecting elements at regular intervals through that ordered list.
Systematic sampling involves a random start and then proceeds with the
selection of every kth element from then onwards. In this case, k =
(population size/sample size). It is important that the starting point is not
automatically the first in the list, but is instead randomly chosen from within
the first to the kth element in the list. A simple example would be to
select every 10th name from the telephone directory (an 'every 10th' sample,
also referred to as 'sampling with a skip of 10').
As long as the starting
point is randomized, systematic sampling is a type of probability sampling. It
is easy to implement and the stratification induced can make it efficient, ifthe
variable by which the list is ordered is correlated with the variable of interest.
'Every 10th' sampling is especially useful for efficient sampling from
databases.
For example, suppose we
wish to sample people from a long street that starts in a poor are (house No.
1) and ends in an expensive district (house No. 1000). A simple random
selection of addresses from this street could easily end up with too many from
the high end and too few from the low end (or vice versa), leading to an
unrepresentative sample. Selecting (e.g.) every 10th street number along the
street ensures that the sample is spread evenly along the length of the street,
representing all of these districts. (Note that if we always start at house #1
and end at #991, the sample is slightly biased towards the low end; by randomly
selecting the start between #1 and #10, this bias is eliminated.
However, systematic
sampling is especially vulnerable to periodicities in the list. If periodicity
is present and the period is a multiple or factor of the interval used, the
sample is especially likely to be unrepresentative of the overall
population, making the scheme less accurate than simple random sampling.
For example, consider a
street where the odd-numbered houses are all on the north (expensive) side of
the road, and the even-numbered houses are all on the south (cheap) side. Under
the sampling scheme given above, it is impossible to get a representative
sample; either the houses sampled will all be from the odd-numbered,
expensive side, or they will all be from the even numbered, cheap side.
Another drawback of
systematic sampling is that even in scenarios where it is more accurate than
SRS, its theoretical properties make it difficult to quantify that
accuracy. (In the two examples of systematic sampling that are given above,
much of the potential sampling error is due to variation between neighbouring
houses - but because this method never selects two neighbouring houses, the
sample will not give us any information on that variation.)
As described above,
systematic sampling is an EPS method, because all elements have the same
probability of selection (in the example given, one in ten). It is not 'simple
random sampling' because different subsets of the same size have different
selection probabilities - e.g. the set {4, 14, 24,..., 994} has a one-in-ten
probability of selection, but the set {4,13,24,34,...} has zero probability of
selection.
Systematic sampling can
also be adapted to a non-EPS approach; for an example, see discussion of PPS
samples below.
Stratified Sampling
Where the population
embraces a number of distinct categories, the frame can be organized by these
categories into separate "strata." Each stratum is then sampled as an
independent subpopulation, out of which individual elements can be randomly
selected. There are several potential benefits to stratified sampling.
First, dividing the
population into distinct, independent strata can enable researchers to draw inferences
about specific subgroups that may be lost in a more generalized random sample.
Second, utilizing a
stratified sampling method can lead to more efficient statistical estimates (provided
that strata are selected based upon relevance to the criterion in question,
instead of availability of the samples). Even if a stratified sampling approach
does not lead to increased statistical efficiency, such a tactic will not
result in less efficiency than would simple random sampling, provided that each
stratum is proportional to the group's size in the population.
Third, it is sometimes
the case that data are more readily available for individual, preexisting strata
within a population than for the overall population; in such cases, using a
stratified sampling approach may be more convenient than aggregating data
across groups (though this may potentially be at odds with the previously noted
importance of utilizing criterion-relevant strata).
Finally, since each
stratum is treated as an independent population, different sampling approaches
can be applied to different strata, potentially enabling researchers to use the
approach best suited (or most cost-effective) for each identified subgroup
within the population.
There are, however,
some potential drawbacks to using stratified sampling. First, identifying strata
and implementing such an approach can increase the cost and complexity of
sample selection, as well as leading to increased complexity of population estimates.
Second, when examining multiple criteria, stratifying variables may be related
to some, but not to others, further complicating the design, and potentially
reducing the utility of the strata. Finally, in some cases (such as designs
with a large number of strata, or those with a specified minimum sample size
per group), stratified sampling can potentially require a larger sample than
would other methods (although in most cases, the required sample size would be
no larger than would be required for simple random sampling.
A stratified sampling
approach is most effective when three conditions are met
1. Variability within
strata are minimized
2. Variability between
strata are maximized
3. The variables upon
which the population is stratified are strongly correlated with the desired dependent
variable.
Advantages over other
sampling methods
1. Focuses on important
subpopulations and ignores irrelevant ones.
2. Allows use of
different sampling techniques for different subpopulations.
3. Improves the
accuracy/efficiency of estimation.
4. Permits greater
balancing of statistical power of tests of differences between strata by sampling
equal numbers from strata varying widely in size.
Disadvantages
1. Requires selection
of relevant stratification variables which can be difficult.
2. Is not useful when
there are no homogeneous subgroups.
3. Can be expensive to
implement.
Post stratification
Stratification is
sometimes introduced after the sampling phase in a process called
"post stratification".
This approach is typically implemented due to a lack of prior knowledge of an appropriate
stratifying variable or when the experimenter lacks the necessary information
to create a stratifying variable during the sampling phase. Although the method
is susceptible to the pitfalls of post hoc approaches, it can provide several
benefits in the right situation. Implementation usually follows a simple random
sample. In addition to allowing for stratification on an ancillary variable, Post
stratification can be used to implement weighting, which can improve the
precision of a sample's estimates.
Oversampling
Choice-based sampling
is one of the stratified sampling strategies. In choice-based sampling, the
data are stratified on the target and a sample is taken from each stratum so
that the rare target class will be more represented in the sample. The model is
then built on this biased sample. The effects of the input variables on the
target are often estimated with more precision with the choice-based sample
even when a smaller overall sample size is taken compared to a random sample.
The results usually must be adjusted to correct for the oversampling.
Probability-Proportional-To-Size
Sampling
In some cases the
sample designer has access to an "auxiliary variable" or "size
measure", believed to be correlated to the variable of interest, for each
element in the population. These data can be used to improve accuracy in sample
design. One option is to use the auxiliary variable as a basis for
stratification, as discussed above.
Another option is
probability-proportional-to-size ('PPS') sampling, in which the selection probability
for each element is set to be proportional to its size measure, up to a maximum
of 1. In a simple PPS design, these selection probabilities can then be used as
the basis for Poisson sampling.
However, this has the
drawback of variable sample size, and different portions of the population may
still be over- or under-represented due to chance variation in selections. To
address this problem, PPS may be combined with a systematic approach.
Example: Suppose we
have six schools with populations of 150, 180, 200, 220, 260, and 490 students
respectively (total 1500 students), and we want to use student population as
the basis for a PPS sample of size three. To do this, we could allocate the
first school numbers 1 to 150, the second school 151 to 330 (= 150 + 180), the
third school 331 to 530, and so on to the last school
(1011 to 1500). We then
generate a random start between 1 and 500 (equal to 1500/3) and count through
the school populations by multiples of 500. If our random start was 137, we
would select the schools which have been allocated numbers 137, 637, and 1137,
i.e. the first, fourth, and sixth schools.
The PPS approach can
improve accuracy for a given sample size by concentrating sample on large
elements that have the greatest impact on population estimates. PPS sampling is
commonly used for surveys of businesses, where element size varies greatly and
auxiliary information is often available - for instance, a survey attempting to
measure the number of guest-nights spent in hotels might use each hotel's number
of rooms as an auxiliary variable. In some cases, an older measurement of the
variable of interest can be used as an auxiliary variable when attempting to produce
more current estimates.
Cluster Sampling
Sometimes it is more
cost-effective to select respondents in groups ('clusters'). Sampling is often
clustered by geography, or by time periods. (Nearly all samples are in some
sense 'clustered' in time - although this is rarely taken into account in the
analysis.) For instance, if surveying households within a city, we might choose
to select 100 city blocks and then interview every household within the
selected blocks.
Clustering can reduce
travel and administrative costs. In the example above, an interviewer can make
a single trip to visit several households in one block, rather than having to
drive to a different block for each household.
It also means that one
does not need a sampling frame listing all elements in the target population.
Instead, clusters can be chosen from a cluster-level frame, with an
element-level frame created only for the selected clusters. In the example
above, the sample only requires a block-level city map for initial selections,
and then a household-level map of the 100 selected blocks, rather than a
household-level map of the whole city.
Cluster sampling
generally increases the variability of sample estimates above that of simple
random sampling, depending on how the clusters differ between themselves, as
compared with the within-cluster variation. For this reason, cluster sampling
requires a larger sample than
SRS to achieve the same
level of accuracy - but cost savings from clustering might still make this a cheaper
option.
Cluster sampling is
commonly implemented as multistage sampling. This is a complex form of cluster
sampling in which two or more levels of units are embedded one in the other.
The first stage consists of constructing the clusters that will be used to sample
from. In the second stage, a sample of primary units is randomly selected from
each cluster (rather than using all units contained in all selected clusters).
In following stages, in each of those selected clusters, additional samples of
units are selected, and so on. All ultimate units (individuals, for instance)
selected at the last step of this procedure are then surveyed. This technique,
thus, is essentially the process of taking random subsamples of preceding
random samples.
Multistage sampling can
substantially reduce sampling costs, where the complete population list would
need to be constructed (before other sampling methods could be applied). By eliminating
the work involved in describing clusters that are not selected, multistage
sampling can reduce the large costs associated with traditional cluster
sampling.
Quota Sampling
In quota sampling, the
population is first segmented into mutually exclusive sub-groups, just as in
stratified sampling. Then judgement is used to select the subjects or units
from each segment based on a specified proportion. For example, an interviewer
may be told to sample 200 females and 300 males between the age of 45 and 60.
It is this second step
which makes the technique one of non-probability sampling. In quota sampling
the selection of the sample is non-random. For example interviewers might be
tempted to interview those who look most helpful. The problem is that these
samples may be biased because not everyone gets a chance of selection. This
random element is its greatest weakness and quota versus probability has been a
matter of controversy for several years.
Accidental Sampling
Accidental sampling
(sometimes known as grab, convenience or opportunity sampling) is a type of non
probability sampling which involves the sample being drawn from that part of
the population which is close to hand. That is, a population is selected because
it is readily available and convenient. It may be through meeting the person or
including a person in the sample when one meets them or chosen by finding them
through technological means such as the internet or through phone. The researcher
using such a sample cannot scientifically make generalizations about the total
population from this sample because it would not be representative enough. For
example, if the interviewer were to conduct such a survey at a shopping center early
in the morning on a given day, the people that he/she could interview would be
limited to those given there at that given time, which would not represent the
views of other members of society in such an area, if the survey were to be
conducted at different times of day and several times per week. This type of
sampling is most useful for pilot testing. Several important considerations for
researchers using convenience samples include:
1. Are there controls
within the research design or experiment which can serve to lessen the impact
of a non-random convenience sample, thereby ensuring the results will be more representative
of the population?
2. Is there good reason
to believe that a particular convenience sample would or should respond or
behave differently than a random sample from the same population?
3. Is the question
being asked by the research one that can adequately be answered using a convenience
sample?
In social science
research, snowball sampling is a similar technique, where existing study subjects
are used to recruit more subjects into the sample. Some variants of snowball
sampling, such as respondent driven sampling, allow calculation of selection probabilities
and are probability sampling methods under certain conditions.
4. Duplication.
Sample Size
Sample size is the
number of items to be selected from the universe. It should be optimum.
Formulas, tables, and
power function charts are well known approaches to determine sample size.
Steps for Using Sample
Size Tables
1. Postulate the effect
size of interest, α, and β.
2. Check sample size
table
1. Select the table
corresponding to the selected α
2. Locate the row
corresponding to the desired power
3. Locate the column
corresponding to the estimated effect size.
4. The intersection of the
column and row is the minimum sample size required.
The Factors Considering
While Deciding The Size Of The Sample
a) Nature of the
population.
b) Complexity of
tabulation.
c) Problem relate with
collection of data.
d) Type of sampling.
e) Basic information.
f) Degree of accuracy
required for the study.
Characteristics of Good
Sample Design
a. Representative.
b. Viable.
c. The selected sample
design should not cause more errors.
d. A good sample design
able to control systematic bias efficiently.
e. If the sample is
well design and selected, decision makers can use this info with confidence.
Criteria of Selecting a
Sampling Procedure
1. Nature of the
problem.
2. Goal of researchers.
3. Geographical area
covered by the survey.
4. Size of the
population under study.
5. Extent of fact
available about population.
6. Availability of
funds
7. Available time for
study.
8. Desired reliability
of the result.
Criteria Used For
Selecting Sampling Techniques
The purpose of the
survey.
Measurability.
Degree of precision.
Information about
population.
The nature of the
population.
The geographical area
covered by the survey.
Fund availability.
Time.
Economy.
Errors in Sample
Surveys
Survey results are
typically subject to some error. Total errors can be classified into sampling
errors and non-sampling errors. The term "error" here includes
systematic biases as well as random errors.
Business Research
Methods 37
Sampling Errors and
Biases
Sampling errors and
biases are induced by the sample design. They include:
1. Selection Bias: When
the true selection probabilities differ from those assumed in
calculating the
results.
2. Random Sampling
Error: Random variation in the results due to the elements in the
sample being selected
at random.
Sampling Bias
Sampling analysis
involve to type of cost namely cost of collecting data and cost of an
incorrect inference
resulting from the data. They are to causes for incorrect inference resulting
from data. They are
i. Systematic bias
ii. Sampling errors
Causes of systematic
bias
Unsuitable sample
frame or source list.
Faulty measuring
device.
Non respondent
Indeterminacy
principle.
Usual bias in
reporting data.
Sampling errors
The errors which arise
due to the use of sampling survey are known as sampling errors. These are random
variation in the sample estimate around the true population parameters.
Type of sampling errors
Biased errors: These
errors are occurring due to the faulty selection of sampling method due to the prejudice
of the researchers.
Unbiased errors: This
type of bias is occurring due to chance difference between the items included
in the sample.
Causes of bias
Bias may arise due to,
1. Faulty process
selection.
2. Faulty work during
the collection of information.
3. Faulty method of
analysis.
Non-Sampling Error
Non-sampling errors are
other errors which can impact the final survey estimates, caused by problems in
data collection, processing, or sample design. They include:
1. Over coverage:
Inclusion of data from outside of the population.
2. Under coverage:
Sampling frame does not include elements in the population.
3. Measurement error:
e.g. when respondents misunderstand a question, or find it difficult to answer.
4. Processing error:
Mistakes in data coding.
5. Non-response:
Failure to obtain complete data from all selected individuals.
After sampling, a
review should be held of the exact process followed in sampling, rather than that
intended, in order to study any effects that any divergences might have on
subsequent analysis.
A particular problem is
that of non-response.
Two major types of
non-response exist: unit non-response (referring to lack of completion of any
part of the survey) and item non-response (submission or participation in
survey but failing to complete one or more components/questions of the survey).
In survey sampling, many of the individuals identified as part of the sample
may be unwilling to participate, not have the time to participate (opportunity
cost), or survey administrators may not have been able to contact them. In this
case, there is a risk of differences, between respondents and non respondents,
leading to biased estimates of population parameters. This is often addressed
by improving survey design, offering incentives, and conducting follow-up
studies which make a repeated attempt to contact the unresponsive and to
characterize their similarities and differences with the rest of the frame. The
effects can also be mitigated by weighting the data when population benchmarks
are available or by imputing data based on answers to other questions.
Non-response is
particularly a problem in internet sampling. Reasons for this problem include improperly
designed surveys,] over-surveying (or survey fatigue), and the fact that
potential participants hold multiple e-mail addresses, which they don't use
anymore or don't check regularly.
Sampling and Data
Collection
Good data collection
involves:
Following the defined
sampling process
Keeping the data in
time order
Noting comments and
other contextual events
Methods of Data
Collection
1. Primary data
collection
2. Secondary data
collection
Collection of Primary
Data
Primary data are those
data which are collected for the first time and these are in original in
character.
Methods of Collecting
Primary Data
1. Observation
2. Interview
3. Questionnaire
4. Schedule
5. Experimentation
6. Simulation
7. Use of telephone
8. Panel method
9. Mail survey
10. Projective
technique
11. Sociometry
12. Focus group
discussion
13. Content analysis
Observation
Observation is the systematic
viewing of specific phenomenon in its proper setting for the specific purpose
of gathering data for a particular study.
Features of observation
• Physical & mental
activity
• Selective
• Purposive & not
informal
• Grasps the
significant events & occurrences
• Should be exact &
based on standardized tools of research
Types of observation
1. Simple and
systematic
2. Subjective and
objective
3. Casual and
scientific
4. Intra subjective and
inter subjective
5. Factual and
inferential
6. Direct and indirect
7. Participant and non
participant
8. Structured and
unstructured
Advantages
• Actual or habits of
person are observed
• Obtain information
from those who are unable to effectively communicate in written or oral form
• No better way to
gather information than through observation
• Most reliable method
of data collection
Disadvantages
• Result of observation
depends on the skill of the observer
• Options and attitudes
cannot be obtained by observation
• It should be
expensive to tie up personnel in such tasks
• The researcher’s
findings are limited to those observed
Component of process of
observation
1. Sensation
2. Attention
3. perception
Experimental method
it is the least used
method for collecting primary data. This method is commonly used
by marketers in test
marketing.
Types;
1. Laboratory
experiments
2. Field experiments
Laboratory experiment
A laboratory experiment
is an investigation conducted in situation created specifically for that purpose
Field experiment
This is an experiment
conducted in real life situation in which the experiments manipulate an independent
variable in order to test a hypothesis
Advantages of
experimental method • the power to determine the causal
relationship between variables is more compared with other methods
• The human errors can
be reduced to the minimum
• It helps to produce
exact measurement
Limitations of
experimental method
• Difficult to
establish comparable control & experimental group
• Limited scope
• Lacks realism
• Cannot be used for
future study
• Not used for
determine opinion ,motive & intention of individual
Simulation
Simulation is a recent
research technique. It is a realistic enactment of roles in an imagined situation.
There are three uses;
1. Assessment of a
situation
2. Understanding a
situation
3. Decision making in a
situation
Types of Simulation
1. Computer simulation
2. Man simulation
3. Man computer
simulation
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