marketing research ch-4
TRANSCRIPT
CHAPTER FOUR
RESEARCH DESIGNS
INTRODUCTION
Research design is a plan of collecting and
analyzing data in an economic, efficient and relevant manner.
It is a plan of organizing framework for doing the study and collecting the necessary data.
is the conceptual structure with in which research is conducted.
It constitutes the blue print for collection, measuring and analysis of data.
Is the blueprint to include:experiments, interviews, observation, and
the analysis of records, simulation, or some combination of these?
is the plan and structure of investigation so considered as to obtain answers to research questions.
The plan is the overall scheme or program of the research.
includes an outline of what the Investigator will do from writing hypotheses and their operational implications to the final analysis of data.
It expresses both the structure of the research problem and the plan of investigation used to obtain empirical evidence on relations of the problem.
These definitions differ in detail, but together they give the essentials of a good research design.
First, the design is a plan for selecting the sources and types of information relevant to the research question.
Second, it is a framework for specifying the relationships among the study's variables.
Third, it is a blueprint for outlining all of the procedures from the hypotheses to the analysis of data.
The design provides for answers to such questions as: What techniques will be used to
gather data? What kind of sampling will be
used? How will time and cost
constraints be dealt with?
Need for research design:Research design is necessary because:• It facilitates the smooth sailing/navigating of the
research operation• It makes research project as efficient as possible
and • help to yield maximum information with
minimum expenditure, time and effort.
• It helps the researcher to organize his ideas in a form where by it will be possible for him to look for flaws and inadequacies
• Design will be given to others for their comment and critical evaluation.
• In absence of such course of action, it will be difficult for the critics to provide comprehensive review of the proposed study.
Features of research design:Important features of a good research design can
be summarized as follow:• It is a plan that contain a clear statement of the
research problem and specifies the source and types of information relevant to the research problem
• It is a strategy specifying which approach will be used for gathering the data or the relevant information
• Indicate the population to be studied and methods to be used in processing and analyzing the data
• It also tentatively includes the time and cost budgets, since most studies are done under these two constraints.
4.1 DECISION MAKING IN PLANNING RESEARCH STRATEGIES AND TACTICS:
Decision making is the process of resolving a problem or choosing among alternative opportunities.
The key to decision making is to recognize the nature of the problem/opportunity, to identify how much information is available, and to recognize what information is needed.
Every business problem or decision-making situation can be classified on a continuum ranging from complete certainty to absolute ambiguity.
Certainty: the decision maker has all the information that he or she
needs. The decision maker knows the exact nature of the business
problem or opportunity. Uncertainty: managers grasp/understand the general nature of the
objectives they wish to achieve, but the information about alternatives is incomplete.
Predictions about the forces that will shape future events are educated guesses.
Under conditions of uncertainty, effective managers recognize potential value in spending additional time gathering information to clarify the nature of the decision.
Ambiguity: Ambiguity means that the nature of the problem
to be solved is unclear. The objectives are vague and the alternatives
are difficult to define. This is by far the most difficult decision
situation.
Business managers face a variety of decision-making situations.
Under conditions of complete certainty when future outcomes are predictable, business research may be a waste of time.
However, under conditions of uncertainty or ambiguity, business research becomes more attractive to the decision maker.
4.2 IMPORTANT CONCEPTS RELATING TO RESEARCH DESIGN:
i. Dependent variable : If one variable depends upon or a consequence of the other variable is called a dependent variable. Is a variable that is to be predicted or
explained?ii. Independent variable: is a variable that is
expected to influence the dependent variable.
iii. Extraneous variable: Independent variables that are not related to the purpose of a study, but may affect the dependent variable are termed as extraneous variable.
E.g., if some one wants to test the relation ship between intensity of light on the level of productivity, other variables like age of workers, heat in the working place or personal problem of worker may as well affect the level of productivity. Since they are not related to the purpose of a study, they are called extraneous variable.
iv. Control: One important characteristic of a good research design is to minimize the influence or effect of extraneous variables(s).
The technical term ‘control’ is used when we design the study minimizing the effects of extraneous independent variables.
In experimental researches, the term ‘control’ is used to refer to restrain experimental conditions.
v. Confounded relationship: When the dependent variable is not free from the influence of extraneous variable(s), the relationship between the dependent and independent variables is said to be confounded by an extraneous variable(s).
vi. Experimental and non-experimental hypothesis-testing research:
When the purpose of research is to test a research hypothesis, it is termed as hypothesis-testing research.
It can be of the experimental design or of the non-experimental design.
Research in which the independent variable is manipulated is termed ‘experimental hypothesis-testing research’ and
a research in which an independent variable is not manipulated is called ‘non-experimental hypothesis-testing research’.
vii. Experimental and control groups: In an experimental hypothesis-testing research when
a group is exposed to usual conditions, it is termed a ‘control group.
But when the group is exposed to some novel/un usual or special condition, it is termed an ‘experimental group’.
It is possible to design studies which include only experimental groups or studies which include both experimental and control groups
viii. Causation and Correlation Causation refers to the relationship
between two or more variables. The variables are different and they are
dependent and independent. The dependent variable is the outcome, the
variable being affected by the independent variable.
The independent variable is the cause that brings about a change in dependent variable.
Causation involves the direction and/or the magnitude of change that the independent variable causes on the dependent variable.
Correlation refers to the regular relationship between the dependent and the independent variables.
Correlation, however, does not necessarily show cause and effect relationship between two sets of variables or occurrences.
Two or more variables may be correlated directly or indirectly.
However, the correlation does not show any causal relationship.
Finding out whether a correlation between variables has causal relationships involves using controls, which means holding some variables constant in order to look at the effect of one independent variable on the others.
When we use experimentation in social science, including business, there are two groups; control group and experimental groups.
Experimental groups are those on which the intervening variables are applied.
The control groups are held, as they are with out applying the intervening variable.
The degree of change or effect that may be observed on the experimental group is likely to be caused by the independent variable.
The control group is held as it is; free from intervention, and the other group is the experimental group, on which the application of the intervening variable is made.
So, measurement is made on both groups before application and after application of the independent variable(s).
If the measurement result before application is assumed to be similar, the difference in the second measurement after the application of the variable(s) is likely to be attributed to the independent variable(s).
Therefore, the net difference is the change manipulated by the independent variables and the common difference in measurement is likely to be caused by other variables.
ix. Validity: refers to the problem of whether the data
collected is the true picture of what is being studied.
It is an evidence of what claims to be evidence. The problem arises particularly when the data
collected seems to be a product of the research method used rather than of what is being studied.
x. Representativeness: refers to the question of whether the
characteristics of a sample drawn properly represents the characteristics of the population from which the sample is selected and about which a conclusion is to be made.
This implies careful planning of the sampling design so that parameter and statistic are similar.
xi. Reliability: refers to the dependability of the research
findings that they can be repeated either by the researcher or by other researchers using similar research methods or procedures.
xii. Treatments: The different condition under which experimental
and controlled groups are put are referred to as treatment.
The usual study program and the special study program are an example of two treatments in studying the effects new or special study program on performance of students.
xiii. Experiment: The process of examining the truth of a statistical
hypothesis, relating to some problem E.g., examining the usefulness of a newly
developed drug is an example of an experiment.
Experiment can be comparative or absolute experiment.
If we want to determine the impact of newly developed drug against the existing drug is an example of comparative experiment.
But the previous example is an example of absolute experiment.
xiv. Experimental unit: the pre-determined plots (or blocks or group) where different treatments are used are known experimental units.
Problemdiscovery
Problem definition(statement of
research objectives)
Secondary(historical)
data
Experiencesurvey
Pilotstudy
Casestudy
Selection ofexploratory research
technique
Selection ofbasic research
method
Experiment SurveyObservation Secondary
Data StudyLaboratory Field Interview Questionnaire
Selection ofexploratory research
techniqueSampling
Probability Nonprobability
Collection ofdata
(fieldwork)
Editing andcoding
data
Dataprocessing
Interpretationof
findings
Report
DataGathering
DataProcessingandAnalysis
Conclusionsand Report
Research Design
Problem Discoveryand Definition
4.3 THE RESEARCH PROCESS AND THE CONCEPT OF RESEARCH DESIGN Decision Alternatives in the Research Process
4.5 CLASSIFICATION OF RESEARCH
different ways of classifying research. really difficult to propose a single
classification method that fits different disciplines and acceptable by all
For example, some classify research as: theoretical and applied research, descriptive and explanatory research, quantitative and qualitative research, conceptual and empirical research, and other types
of research.
Others classify research in a different way.
It should also be noted that there is no clear
dividing line between one method and the
other.
There are always overlaps in a sense that
one method somehow includes the other
Research can be classified in based on:
goal of research,
specific objectives of research,
approaches of research,
designs,
the type of data used in research, and
fields of study.
1. Classification of Research based on the Goal of Research
A. Basic Research and B. Applied ResearchA. Pure/basic scientific Research its primary objective is advancement of
knowledge and the theoretical understanding of the relations among variables.
basically concerned with the formulation of a theory or a contribution to the existing body of knowledge
designed to add to an organized body
of scientific knowledge and
does not necessarily produce results of
immediate practical value.
The major aims of basic research is:
Obtaining and using empirical data to
formulate, expand, or evaluate theory;
and
Discovery of knowledge solely for the
sake of knowledge.
Hence, basic research may take any of the following forms:
Discovery: where a totally new idea or explanation
emerges from empirical research which may
revolutionize thinking on that particular topic.
Invention : where a new technique or method is
created.
Reflection : where an existing theory, technique or
group of ideas is re-examined possibly in a different
organizational or social context.
B. Applied Scientific Research is designed to solve practical problems than to
acquire knowledge
the goal is to improve the human condition
undertaken to solve immediate practical problem and the goal of adding to the scientific knowledge is secondary.
The purpose of applied research is about testing theories, often generated by pure science, and
applying them to real situations, addressing more than just abstract principles.
Applied scientific research can be about finding out the answer to a specific problem, such as: ‘Is global warming avoidable?’ or ‘Does a new type of medicine really help the
patients?’
2. Classification of Research based on the Specific Objectives of Research
A. DESCRIPTIVE, B. EXPLANATORY AND C. EXPLORATORY RESEARCH
A. Descriptive sets out to describe and to interpret what is.
It aims to describe the state of affairs as it
exists at present The methods that come under descriptive research
are:SurveysCorrelation studiesObservation studiesCase studies
i. SURVEY RESEARCH
The most common method of generating
primary data is through surveys.
A survey is a research technique in which
information is gathered from a sample of
people by use of a questionnaire.
The task of:
writing a questionnaire,
determining the list of questions, and
designing the exact format of the printed or
written questionnaire
is an essential aspect of the development of
a survey research design.
Surveys ask respondents for information using verbal or written questioning.
Respondents are a representative sample of people
Gathering Information via Surveys has following Advantages • Quick• Inexpensive Problems:• Efficient Poor Design• Accurate Improper Execution• Flexible
:
Total error
Systematic error (bias)
Random sampling error
Diagram of Total Survey Error
Random Sampling Error
A statistical fluctuation that occurs because of change variation in the elements selected for the sample
Systematic Error:
Systematic error results from some imperfect aspect of the research design or from a mistake in the execution of the research.
Systematic error (bias)
Administrativeerror
Respondenterror
Diagram of Total Survey Error
Sample Bias Sample bias - when the results of a sample show a
persistent tendency to deviate in one direction from the true value of the population parameter.
Diagram of Total Survey Error
Respondenterror
Non-responseerror
Responsebias
Respondent Error:A classification of sample bias resulting from some
respondent action or inaction• Non-response bias• Response bias
Non-response Error:• Non-respondents - people who refuse to cooperate• Not-at-homes• Self-selection bias
• Over-represents extreme positions• Under-represents indifference
Diagram of Total Survey Error
Responsebias
Unconsciousmisrepresentation
Deliberatefalsification
Response Bias
A bias that occurs when respondents tend to answer questions with a certain slant/angle that consciously or unconsciously misrepresents the truth.
Acquiescence bias
Extremity bias
Interviewer bias
Auspices bias
Social desirability bias
Total Survey Error
A. Acquiescence Bias: A category of response bias that results because
some individuals tend to agree with all questions or to concur with a particular position.
B. Extremity Bias: A category of response bias that results because
response styles vary from person to person; some individuals tend to use extremes when responding to questions.
C. Interviewer Bias:A response bias that occurs because the presence of
the interviewer influences answers.
D. Auspices /sponsorship Bias: Bias in the responses of subjects caused by the
respondents being influenced by the organization conducting the study.
E. Social Desirability Bias: Bias in responses caused by respondents’ desire, either
conscious or unconscious, to gain prestige or appear in a different social role.
Administrative Error:• Improper administration of the research task• Blunders
• Confusion• Neglect• Omission
Diagram of Total Survey Error
Data processing error
Sample selection error
Interviewer error
Interviewer cheating
A. Interviewer cheating – filling in fake answers or falsifying
interviewers B. Data processing error
incorrect data entry, computer programming, or other procedural errors during the analysis stage.
C. Sample selection error improper sample design or sampling
procedure execution.D. Interviewer error
field mistakes.
II. CORRELATIONAL STUDIES
trace relationships among two or more
variables in order to gain greater
situational insight.
with the aim of studying the
associations among these variables.
The purpose is not to establish cause-effect
relationship among variables but to
determine whether the variables under study
have some kind of association or not.
Variables being studied may have positive
or negative relationship or they may not
have relationship at all.
4.5.2 OBSERVATIONAL RESEARCH:
In many situations the objective of the research
project is merely to record what can be observed
for example, the number of automobiles that pass
a site for a proposed gasoline station.
This can be mechanically recorded or observed by
any person.
The amount of time it takes an employee to
perform a task may be observed in a time-
and-motion study.
Research personnel, known as "mystery
shoppers," may act as customers to observe
the actions of sales personnel or do
"comparative shopping" to learn the prices
charged at competitive outlets.
The main advantage of the observation technique is that it records behavior without relying on reports from respondents.
Observational methods are often non reactive because data, are collected indirectly and passively without a respondent's direct participation.
Observation is more complex than mere "nose counting," and the task is more difficult to administer than the inexperienced researcher would imagine.
Several things of interest simply cannot be observed.
Attitudes, opinions, motivations, and other intangible states of mind cannot be recorded by using the observation method.
“YOU SEE, BUT YOU DO NOT OBSERVE.”
Sherlock Holmes Scientific Observation Is Systematic.
Phenomena Example
Human behavior or physical action
Shoppers movement pattern in a store
Verbal behavior Statements made by airline travelers who wait in line
Expressive behavior Facial expressions, tone of voice, and other form of body language
Spatial relations and locations
How close visitors at an art museum stand to paintings
What Can Be Observed
Temporal patterns How long fast-food customers wait for their order to be served
Physical objects What brand name items are stored in consumers’ pantries
Verbal and Pictorial Records Bar codes on product packages
Categories of Observation:• Human versus mechanical• Visible versus hidden• Direct• Contrived
Observation of Human Behavior Benefits:
• Communication with respondent is not
necessary
• Data without distortions due to self-report
(e.g.: without social desirability) Bias
• No need to rely on respondents memory
• Nonverbal behavior data may be obtained
• Certain data may be obtained more quickly
• Environmental conditions may be recorded
• May be combined with survey to provide
supplemental evidence.
Observation of Human Behavior Limitations:
• Cognitive phenomena cannot be observed• Interpretation of data may be a problem• Not all activity can be recorded• Only short periods can be observed• Observer bias possible• Possible invasion of privacy
Observation of Physical Objects: Physical-trace evidence Wear and tear of a book indicates how often it has
been read. Scientifically Contrived Observation:The creation of an artificial environment to test a
hypothesis. Response Latency: Recording the decision time necessary to make a
choice between two alternatives It is presumed to indicate the strength of
preference between alternatives.
Content Analysis:
Obtains data by observing and analyzing the
content of advertisements, letters, articles, etc.Deals with the study of the message itself
Measures the extent of emphasis or omission
Mechanical Observation:Traffic Counters
Web Traffic
Scanners
IV. Case Studiesis a very popular form of qualitative analysisinvolves a careful and complete observation of a social unit, be that unit a person, a family, an institution, a cultural group or even the entire community. It is a method of study in depth rather than breadth. It places more emphasis on the full analysis of a limited number of events or conditions and their interrelations.
deals with the processes that take place and
their interrelationship.
Thus, is essentially an intensive
investigation of the particular unit under
consideration.
The object of the case study method is to
locate the factors that account for the
behavior patterns of the given unit as an
integrated totality.
is a form of qualitative analysis where in
careful and complete observation of an
individual or a situation or an institution is
done;
efforts are made to study each and every
aspect of the concerning unit in minute
details and then from case data
generalizations and inferences are drawn.
Assumptions: The case study method is based on several assumptions.
(i) The assumption of uniformity in the basic human nature in spite of the fact that human behavior may vary according to situations.
(ii) The assumption of studying the natural history of the unit concerned.
(iii) The assumption of comprehensive study of the unit concerned.
Major phases involved: Major phases involved in case study are as follows:
(i) Recognition and determination of the status of the phenomenon to be investigated or the unit of attention.
(ii) Collection of data, examination and history of the given phenomenon.
(iii)Diagnosis and identification of causal factors as a basis for remedial or developmental treatment.
(iv) Application of remedial measures i.e., treatment and therapy (this phase is often characterized as case work).
(v) Follow-up program to determine effectiveness of
the treatment applied.
Advantages:
1. Being an exhaustive study of a social unit, the case study
method enables us to understand fully the behavior pattern of
the concerned unit.
2. a researcher can obtain a real and progressive record of
personal experiences which would reveal man’s inner
strivings, tensions and motivations that drive him to action
along with the forces that direct him to adopt a certain pattern
of behavior.
3. enables the researcher to trace out the natural
history of the social unit and its relationship with
the social factors and the forces involved in its
surrounding environment.
4. It helps in formulating relevant hypotheses along
with the data which may be helpful in testing
them. Case studies, thus, enable the generalized
knowledge to get richer and richer.
5. The method facilitates intensive study of social units which is generally not possible if we use either the observation method or the method of collecting information through schedules.
This is the reason why case study method is being frequently used, particularly in social researches.
6. Information collected under the case study method helps a
lot to the researcher in the task of constructing the
appropriate questionnaire or schedule for the said task
requires thorough knowledge of the concerning universe.
7. The researcher can use one or more of the several research
methods depth interviews, questionnaires, documents, study reports of individuals, letters, and the like is possible under case study method.
8. enhances the experience of the researcher and
this in turn increases his analyzing ability and
skill.
9. is a means to well understand the past of a social
unit because of its emphasis of historical analysis.
Limitations:
1. Case situations are seldom comparable and
as such the information gathered in case studies
is often not comparable. Since the subject
under case study tells history in his own words,
logical concepts and units of scientific
classification have to be read into it or out of it
by the investigator.
2. Read Bain does not consider the case data
as significant scientific data since they do not
provide knowledge of the “impersonal,
universal, non-ethical, non-practical,
repetitive aspects of phenomena.”
Real information is often not collected
because the subjectivity of the researcher
does enter in the collection of information
in a case study.
3. The danger of false generalization is always there
in view of the fact that no set rules are followed in
collection of the information and only few units are
studied.
4. It consumes more time and requires lot of
expenditure.
More time is needed under case study method since
one studies the natural history cycles of social units
and that too minutely.
5. The case data are often vitiated
because the researcher, may write what
he thinks
6. Case study method is based on several
assumptions which may not be very
realistic at times, and as such the
usefulness of case data is always
subject to doubt.
7. Case study method can be used
only in a limited sphere., it is not
possible to use it in case of a big
society.
Sampling is also not possible under
a case study method.
8. Response of the investigator is an important limitation of the case study method. He often thinks that he has full knowledge
of the unit and can himself answer about it. In case the same is not true, then
consequences follow. In fact, this is more the fault of the
researcher rather than that of the case method.
Despite the above stated limitations, we find that
case studies are being undertaken in several
disciplines, particularly in sociology, as a tool of
scientific research in view of the several
advantages indicated earlier.
Most of the limitations can be removed if
researchers are always conscious of these and are
well trained in the modern methods of collecting
case data and in the scientific techniques of
assembling, classifying and processing the same.
Besides, case studies, in modern times,
can be conducted in such a manner that
the data are amenable to quantification
and statistical treatment.
Possibly, this is also the reason why
case studies are becoming popular day
by day.
2. Explanatory Research
For issues that are already known and have a description, one might begin to wonder why things are the way they are
The desire to know "why," to explain, is the purpose of explanatory research.
It is a continuation of descriptive research on to identify the reasons for something
that occurs
The researcher goes beyond merely describing the
characteristics, to analyse and explain why or how
something is happening.
Thus, explanatory or analytical research aims to
understand phenomena by discovering and
measuring causal relations among them.
That is, explanatory research looks for causes and
reasons
For example, it is one thing to describe the crime
rate in a country, to examine trends over time or
to compare the rates in different countries.
It is quite a different thing to develop explanations
about :
why the crime rate is as high as it is
why some types of crime are increasing or
why the rate is higher in some countries than
in others.
Explanatory research builds on both exploratory
and descriptive researches. It involves:Explaining things not just reporting. Why? Elaborating
and enriching a theory's explanation.
Determining which of several explanations is best.
Determining the accuracy of the theory; test a theory's
predictions or principle.
Providing evidence to support or refute/disprove an
explanation or prediction.
Testing a theory's predictions or principles.
Answering the why questions involves developing causal
explanations.
Causal explanations argue that phenomenon Y is affected
by factor X.
In this example, the cause or the reason is X which is
technically termed as independent variable and the effect
or the behaviour is Y which is also known as dependent
variable.
Some causal explanations will be simple while others will
be more complex.
There are two types of explanatory research:
A. Experimental research
B. Ex post facto researchA. EXPERIMENTAL RESEARCH
Experimental studies are those in which the
researcher can control and manipulate at least one of
the independent variable and test the hypothesis of
causal relationship between variables.
experimental research involves comparing two
groups on one outcome measure to test some
hypothesis regarding causation.
The key element in true experimental research is scientific control and the ability to rule out alternative explanations.
It is also called as Empirical Research or Cause and Effect Method,
it is a data-based research, coming up with conclusions which are capable of being verified with observation or experiment.
Experimental research is appropriate when proof is
sought that certain variables affect other variables in
some way. e.g.Tenderizers ( independent variable) affect cooking time
and texture of meat( dependent variable) .
- The effect of substituting one ingredient in whole or
in part for another such as soya flour to flour for
making high protein bread.
- -Develop recipes to use products.
Such research is characterized by the
experimenter’s control over the variables under
study and the deliberate manipulation of one of
them to study its effects.
In such a research, it is necessary to get at facts
first hand, at their source, and actively go
about doing certain things to stimulate the
production of desired information.
Researcher must provide himself with a
working hypothesis or guess as to the
probable results.
Then work to get enough facts (data) to
prove/verify or disprove the hypothesis.
He then sets up experimental designs which
he thinks will manipulate the persons or the
materials concerned so as to bring forth the
desired information.
Evidence gathered through experimental or empirical studies today is considered to be the most powerful support possible for a given hypothesis.
Basic versus Factorial Experimental Designs
Basic experimental designs
a single independent variable is manipulated to
observe its effect on a single dependent variable.
Factorial experimental designs
More sophisticated than basic experimental
designs.
allow for investigation of the interaction of two or
more independent variables.
B. EX POST FACTO RESEARCH
Ex post facto research is a method of teasing
out possible antecedents of events that have
happened and cannot, therefore, be caused or
manipulated by the investigator.
Ex post facto in research means after the fact
or retrospectively and
Refers to those studies which investigate possible cause-and-effect relationships by observing an existing condition or state of affairs and searching back in time for plausible causal factors.
If a researcher is interested in investigating the reasons why fatal traffic accident is increasing in Ethiopia, he/she cannot do it by randomly assigning research participants into experimental and control group.
There is no way in which a researcher can study the actual accidents because they have happened.
What a researcher can do, however, is to attempt to reconstruct the causal link by studying the statistics, examining the accident spots, and taking note of the statements given by victims and witnesses.
This means that a researcher is studying the independent variable or variables in retrospect/survey for their possible relationship to, and effects on, the dependent variable or variables.
Evaluating Research Designs Researchers argue that there is no one best research
design for all situations. There are no hard-and-fast rules for good business
research. This does not mean that the researcher when faced
with a problem is also faced with chaos and confusion.
It means that the researcher has many alternative methods for solving the problem.
An eminent behavioral researcher has stated this concept quite persuasively: There is never a single, standard, correct
method of carrying out a piece of research. Do not wait to start your research until you find
out the proper approach, because there are many ways to tackle a problem-some good, some bad, but probably several good ways.
There is no single perfect design.
Knowing how to select the most appropriate research design develops with experience.
Inexperienced researchers often jump to the conclusion that the survey method is the best design, because they are most familiar with this method.
Sometimes instead of using an expensive survey, a creative researcher, familiar with other research designs may suggest a far less expensive alternative-an unobtrusive observation technique.
New research designs can be formed by combining the different types of research designs under the principle of triangulation.
For instance, exploratory research design could be formed as a combination of observation method of data collection, ex post design, cross-sectional time dimension, field research design and qualitative analysis.
Likewise, varieties of research designs can be identified by combining the different research designs.
Once an appropriate design has been determined, the researcher moves on to the next stage-planning the sample to be used.
4.6 SAMPLING DESIGNIntroduction:
The statistical investigation can take two forms.
The researcher studies every unit of the field of
study and drive conclusion by computing the sum
of all units.
This type of survey is called census survey.
Or the researcher study only a unit in the field of
survey and this type of survey is called sample
survey.
In sample technique of survey some unit
are taken as representative of the
whole field of domain and the
conclusion of the sample is extended to
the whole population.
4.6.1 Some Fundamental Definitions:
Population: Is the theoretically specified aggregation
of survey elements from which the
survey sample is actually selected.
Sampling Frame: Is the list of elements from which
the sample is drawn
Sample: A subset or some part of a larger population
Sample design: Is a definite plan for obtaining a
sample frame
Sampling: Is the process of using a small number or part of a larger population to make conclusion about the whole population.
Element: Is unit from which information is collected and which provides the basis of analysis
Statistic: Is a characteristic of a sampleParameter: Is a characteristic of a population. E.g., when we work out certain measurement like,
mean from a sample they are called statistic. But when such measure describe the characteristic of the population, they are called parameter(s)
Population mean () is a parameter Where as the sample mean (x) is a statistic
Accuracy And Precision:
Accuracy refers to the amount of deviations of the
estimate from the true value
Precision refers to the size of the deviation by
repeated applications of the sampling procedure.
Precision is usually expressed in terms of the
standard error of the estimator.
Less precision is reflected by a larger standard
error.
The precision and accuracy of survey result are
affected by the manner in which the sample has
been chosen. Strict attention must be paid to the planning of
the sample. Regardless of the type of project to be
conducted, the process of selecting a sample
follows well-defined activities.
4.6.2 Sampling ProcessSteps involved in Sampling procedure:
Defining Population
Census Vs Sample
Sampling Design
Sample Size
Estimate Cost of Planning
Execute Sampling Process
I. DEFINING THE POPULATION The first thing the sample plan must include
is a definition of the population to be investigated.
It implies specifying the subject of the study. Specification of a population involves:
identifying which elements (items) are included,
where and when.
If the research problem is not properly defined then defining population will be difficult.
For example, a financial institution considering making a new type of loan plan available, might acquire information from any one or all of the following groups-
Which element Where WhenAll depositors Bank A For the last 12
months
Depositors who have borrowed money
Bank A For the last 12 months
All people who have borrowed money
Specified geographic area
For the last 12 months
All people Specified geographic area
For the last 12 months
From researcher point of view, each group represents a distinct population.
Thus, the researcher must begin with careful specification of his population.
II. CENSUS VS. SAMPLE Decision about whether the survey is to be
conducted among all members of the population(census) or only a subset of the population(sample).
That is, a choice must be made between census and sample
Advantages of census : Reliability: Data derived through census
are highly reliable.
The only possible errors can be due to
computation of the elements.
Detailed information: Census data yield
much more information.
Limitation of census: Expensiveness: Investigating each elements
of the population is expensive to
any individual researcher
Excessive time and energy: Beside cost
factor, census survey takes too long
time and consumes too much
energy.
Need for sampling: The use of sample in research project
has the objective of: estimating;
testing and
making inference about a population on the
basis of information taken from the sample.
Sampling can save time and money (it is
economical than census).
Sampling may enable more accurate
measurement, because sample study is
generally conducted by trained and
experienced investigator.
Sampling remains the only way when
population contains infinitely many
members.
It usually enables to estimate the sampling error
and, thus, assists obtaining information concerning
some characteristics of the population.
If the choice of sample units is made with due care
and the matter under survey is not heterogeneous,
the conclusion of the sample survey can have
almost the same reliability as those of census
survey.
Sampling technique also enables researchers to
obtain detailed study, as the number of sample
units is fairly small these can be studied intensively
and elaborately.
Limitations of sampling technique: Less accuracy: In comparison to census technique
the conclusion derived from sample are more liable
to error.
Therefore, sampling technique is less accurate than
the census technique.
Misleading conclusion: If the sample is not carefully selected or if samples are arbitrarily selected, the conclusion derived from them will become misleading if extended to all population.In assessing the monthly expenditure of
university students if the selected sample contains more rich students, our result (conclusion) will be erroneous if it extended to all students
Need for specialized knowledge: The sample technique can be successful only if a competent and able scientist makes the selection.
If it is done by average researcher the selection is liable to error.
A beginner researcher commonly asks himself when
and where sampling technique is appropriate to his
study. Sampling technique is used under the following
conditions. Vast data: When the number of units is very large,
sampling technique must be used. Because it economize money, time and effort
When at most accuracy is not required: The sampling technique is very suitable in those situations where 100% accuracy is not required, otherwise census technique is unavoidable.
When census is impossible: If we want to know the
amount of mineral wealth in a country we cannot
dig all mines to discover and count. Rather we
have to use the sampling technique.
Homogeneity: If all units of the population are alike
(similar) sampling technique is easy to use.
Infinite population: If the population is
unlimited sampling technique is imminent.
Essentials of an ideal sample: An ideal sample should fulfill the following
four basic characteristics. Representativeness: An ideal sample must
represent adequately the whole population. It should not lack a quality found in the whole population.
Independence: Each unit should be free to be included in the sample.
Adequacy: The number of units included in the sample should be sufficient to enable derivation of conclusion applicable for the whole population. A sample having 10% of the whole population can be considered.
Homogeneity: The element included in the sample must bear likeness with other element.
III. SAMPLE DESIGN sample design is the heart of sample planning. Specification of sample design includes the
method of selecting individual sample unit involves both theoretical and practical considerations.
Sample design should answer the following
What type of sample to use? Different types of
samples are considered, examined and appropriate
sampling technique is selected. What is the appropriate sample unit? Is a single
element or group of elements of the defined population are subjected to selection in the sample? Sampling unit can be Primary sampling unit: Units selected in the
first stage of sampling Secondary sampling unit: A unit selected in the
second stage of sampling
What frame (list of sampling unit) is available for the population? In actual practice the sample will be drawn from
a list of population elements, which can be different from target population that has been defined.
Sample frame is the list of elements from which the sample is drawn.
It is a physical list of the population elements. Ideally the sample frame should identify each
population element once only once. It should not include elements not in the defined
population.
The most widely used frame in survey research is a telephone directory.
Using such a frame, however, may lead to error arising from exclusion of:
Groups with no telephone Voluntary unlisted Involuntary unlistedHow are refusals and non-response to be handled? The sample plan must include provision for how
refusals and non-response are to be handled. Whether additional sampling units are to be chosen
as replacement and if so, how these are to be selected.
IV. SAMPLE SIZE DETERMINATION A researcher is worried about sample size because of the
fact that sample size (number of elements in sample) and precision of the study are directly related.
The larger the sample size the higher is the accuracy. The sample size determination is purely statistical
activity, which needs statistical knowledge. There are a number of sample size determination
methods. Personal judgments: The personal judgment and
subjective decision of the researcher in some cases can be used as a base to determine the size of the sample.
Budgetary approach is another way to determine the sample size. Under this approach the sample size is determined by the available fund for the proposed study.
E.g., if cost of surveying of one individual or unit is 30
birr and if the total available fund for survey is say 1800
birr , the sample size then will be determined as,
Sample size (n) = Total budget of survey / Cost of unit
survey, accordingly, the sample size will be 60 units
(1800 / 30 = 60 units). Traditional inferences: This is based on precision rate
and confidence level. To estimate sample size using this approach we need to
have information about the estimated variance of the population, the magnitude of acceptable error and the confidence interval.
In addition to the purpose of the study and
population size, three criteria usually will
need to be specified to determine the
appropriate sample size:
the level of precision,
the level of confidence or risk, and
the degree of variability in the attributes
being measured.
1. THE LEVEL OF PRECISION The level of precision, sometimes called
sampling error, is the range in which the true value of the
population is estimated to be. This range is often expressed in percentage
points, (e.g., ±5 percent), in the same way that results for political campaign polls are reported by the media.
Thus, if a researcher finds that 60% of farmers in the sample have adopted a recommended practice with a precision rate of ±5%, then he or she can conclude that between 55% and 65% of farmers in the population have adopted the practice.
2. THE CONFIDENCE LEVEL The confidence or risk level is based on
ideas encompassed under the Central Limit Theorem.
The key idea encompassed in the Central Limit Theorem is that when a population is repeatedly sampled, the average value of the attribute obtained by those samples is equal to the true population value.
Furthermore, the values obtained by these samples are distributed normally about the true value, with some samples having a higher value and some obtaining a lower score than the true population value.
In a normal distribution, approximately 95% of the sample values are within two standard deviations of the true population value (e.g., mean).
In other words, this means that, if a 95% confidence level is selected, 95 out of 100 samples will have the true population value within the range of precision specified earlier.
There is always a chance that the
sample you obtain does not represent
the true population value.
This risk is reduced for 99%
confidence levels and increased for
90% (or lower) confidence levels.
3. DEGREE OF VARIABILITY degree of variability in the attributes being
measured refers to the distribution of attributes in the population.
The more heterogeneous a population, the larger the sample size required to obtain a given level of precision.
The less variable (more homogeneous) a population, the smaller the sample size.
Note that a proportion of 50% indicates a greater level of variability than either 20% or 80%.
This is because 20% and 80% indicate that a large majority do not or do, respectively, have the attribute of interest.
Because a proportion of .5 indicates the maximum variability in a population, it is often used in determining a more conservative sample size, that is, the sample size may be larger than if the true variability of the population attribute were used.
Strategies For Determining Sample Size
There are several approaches to determining the sample size.
These include using a census for small populations, imitating a sample size of similar studies, using published tables, and applying formulas to calculate a sample size.
Each strategy is discussed below.
Using a Census for Small Populations One approach is to use the entire population as
the sample. Although cost considerations make this
impossible for large populations, a census is attractive for small populations (e.g., 200 or less).
A census eliminates sampling error and provides data on all the individuals in the population.
In addition, some costs such as questionnaire design and developing the sampling frame are “fixed,” that is, they will be the same for samples of 50 or 200.
Finally, virtually the entire population would have to be sampled in small populations to achieve a desirable level of precision.
Using a Sample Size of a Similar Study Another approach is to use the same sample
size as those of studies similar to the one you plan.
Without reviewing the procedures employed in these studies you may run the risk of repeating errors that were made in determining the sample size for another study. However, a review of the literature in your discipline can provide guidance about “typical” sample sizes that are used.
Using Published Tables A third way to determine sample size is to rely on
published tables, which provide the sample size for a given set of
criteria. Table 1 and Table 2 present sample sizes that would be necessary for given combinations of precision,
confidence levels, and variability. Please note two things. First,
these sample sizes reflect the number of obtained responses
and not necessarily the number of surveys mailed or
interviews planned (this number is often increased to compensate
for nonresponse). Second, the sample sizes in Table 2
presume that the attributes being measured are distributed
normally or nearly so. If this assumption cannot be met, then the entire population may need to be surveyed.
Using Formulas to Calculate a Sample Size Although tables can provide a useful guide for
determining the sample size, you may need to calculate
the necessary sample size for a different combination of
levels of precision, confidence, and variability. The fourth
approach to determining sample size is the application of
one of several formulas (Equation 5 was used to calculate the
sample sizes in Table 1 and Table 2 ).
Calmorin’s sample size determination formula. n= NZ + (Se) 2 x (1-P) N Se + Z2 x P (1-p) Where;
– n = sample size – N = total number of population – Z= the standard value (2.58) of 1% level of
probability with 0.99 reliability, – Se= Sampling error 1% (0.01)– p = the population proportion
Formula For Calculating A Sample For Proportions For populations that are large, Cochran developed the
Equation to yield a representative sample for proportions. For proportion no = Z2 p.q /e2
Which is valid where no is the sample size, Z2 is the abscissa of the normal curve that cuts off an
area at the tails (1 - equals the desired confidence level, e.g., 95%)1,
e is the desired level of precision, p is the estimated proportion of an attribute that is
present in the population, and q is 1- p. The value for Z is found in statistical tables which
contain the area under the normal curve.
To illustrate, suppose we wish to evaluate a state-wide Extension program in which farmers were encouraged to adopt a new practice. Assume there is a large population but that we do not know the variability in the proportion that will adopt the practice; therefore, assume p=.5 (maximum variability). Furthermore, suppose we desire a 95% confidence level and ±5% precision. The resulting sample size is demonstrated in Equation as:
no = Z2 p.q /e2 = (1.96) 2 (.5) (.5) = 385 farmers(.05) 2
Finite Population Correction For Proportions: If the population is small then the sample size can be
reduced slightly.This is because a given sample size provides
proportionately more information for a small population than for a large population.
The sample size (n0) can be adjusted using Equation:
n0 = n0___ = 385 1+ ( no - 1) 1 + (385-1) = 323 Farmers N 2000
Where n is the sample size and N is the population size.
Suppose our evaluation of farmers' adoption of the new practice only affected 2,000 farmers.
The sample size that would now be necessary is shown in the above calculation.
Formula For Sample Size For The Mean:For mean n = (ZS/E)2
Where as, Z = confident level E= range of error S=standard deviation / (Variance square 2 )
For Example:Suppose a survey researcher, studying
expenditures on lipstick, wishes to have a 95 percent confident level (Z) and a range of error (E) of less than $2.00. The estimate of the standard deviation is $29.00.
2
Ezsn
2
00.200.2996.1
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00.284.56
242.28 808
Suppose, in the same example as the one before, the range of error (E) is acceptable at $4.00, sample size is reduced.
2
Ezsn
2
00.400.2996.1
2
00.484.56
221.14 202
99% ConfidenceCalculating Sample Size
[ ]1389
265.372
253.74
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2)29)(57.2(n
2
]347
[ 6325.182
453.74
2
4)29)(57.2(n
2
The disadvantage of the sample size based on the
mean is that a "good" estimate of the population
variance is necessary.
Often, an estimate is not available.
Furthermore, the sample size can vary widely from
one attribute to another because each is likely to
have a different variance.
Because of these problems, the sample size for
the proportion is frequently preferred.
Bayesian Statistics. This is the selection of
the sample size, which maximizes the
difference between the Expected Value of
Information (EVI) and Cost of Sampling.
That is, Marginal Cost of Information (MCI)
should be equal to Marginal Value of
Information (MVI).
Optimum sample size implies MVI = MCI
V. COST OF SAMPLING The sample plan must take into account the
estimated cost of sampling.
Such costs are of two types, overhead costs
and, variable costs.
In reality however, it may be difficult and
even for some people not reasonable to
separate sampling cost from over all study
cost.
VI. EXECUTION OF SAMPLING PROCESS
Is the last step In short the sample is actually chosen. The actual requirement for sampling
procedure will be Sample must be representative and Sample must be adequate.
4.6.3 SAMPLING TECHNIQUES Sampling techniques are basically of two types
namely, probability sampling and non-probability sampling.
I. PROBABILITY SAMPLING All probability samples are based on chance
selection procedures. Chance selection eliminates the bias inherent
in the non-probability sampling procedure, because this process is random.
The procedure of randomization should
not be thought as unplanned or
unscientific.
It is rather the basis of all probability
sampling technique.
Probability sampling is the most preferred
type of sampling because of the following
characteristics:
The sample units are not selected based on the
desecration of the researcher
Each unit of the population has some known
probability of entering the sample
The processes of sampling is automatic in one or
more steps of selection of units in the sample.
There are number of probability
sampling some of them are discussed
bellow : Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Multi-Stage Sampling
1. SIMPLE RANDOM SAMPLING:is the basic sampling method in every statistical computation. Each element in the population has an equal chance of being included in the sample. is drawn by a random procedure from a sample
frame. Drawing names from a hat is a typical simple random sampling technique. The sampling process is simple because it requires only one stage of sample selection.
Selecting random sample is made in such a way that, each element in the sample frame is assigned a number.
Then each number is written on separate pieces of paper, properly mixed and one is selected.
If say the sample size is 45, then the selection procedure is repeated 45 times.
When the population is consists of a large number of elements table of random digits or computer generated random numbers are utilized.
2. SYSTEMATIC SAMPLING: involves only a slight difference from simple random
sampling. The mechanics of taking a systematic sample are rather
simple. If the population contains N ordered elements, and sample
size of n is required or desired to select, then we find the ratio of these two numbers, i.e., N/n to obtain the sampling interval.
E.g., Say the population size N= 600 and the desired sample size is 60 (n = 60), then the sample interval will be 600/60 = 10
Random number at the 10 interval will be selected, i.e., if the researcher starts from the fourth element then 4th, 14th, 24th etc, elements will be selected.
assumes that the population elements are
ordered in the same fashion (like names in
the telephone directory).
Some types of ordering, such as an
alphabetic listing, will usually be
uncorrelated with the characteristics (say
income family size) to be investigated.
If the arrangement of the elements of the
sample is itself random with regard to the
characteristics under study, systematic
sampling will tend to give result close to
those provided by simple random sampling.
We say close for the reason that, in
systematic sampling all elements of the
population do not have the same or equal
chance of being included.
Systematic sampling may increase
representative-ness when items are ordered
with regard to the characteristics of interest.
E.g., if the population of customer group
are ordered by decreasing order of
purchase volume, a systematic sample will
be sure to contain some high-volume and
some low-volume customers.
The problem of periodicity occurs if a list has a systematic pattern, that is, if the list is not random in character (like cyclical or seasonal pattern).
E.g., consider collecting retail store- sale volume, if the researcher is to choose a sampling interval of seven days, his choice of day can result in sampling that would not reflect day-off- the week variation in sale.
3. STRATIFIED SAMPLING: This method of sampling is a mixture of deliberate and
random sampling technique. If population from which the sample to be drawn does not
constitute a homogeneous group, stratified sampling technique is used in order to obtain a representative sample.
Under this technique, the population is divided into various classes or sub-population, which is individually more homogeneous than the total population.
The different sub-populations are called strata. Then certain items (elements) are selected from the classes
by the random sampling technique.
Since each stratum is more homogeneous than the total population, we are able to get more precise estimate for each stratum.
By estimating more accurately each of the component parts of population (sub population), we get a better estimate of the whole population.
In other words the population will be broken into different strata based on one or more characteristics say, frequency of purchase of a product or types of customers (credit card versus non-credit card), or the industry.
Thus, we will have strata of customers, strata of industry etc.
Suppose a researcher wishes to collect information regarding income expenditure of the male population of, say Desire Town.
First we shall split the whole male population in the town into various strata on the basis of, say special professions like:
Class of service giving people Business men Shop keepers From these different groups the researcher will
select elements using random sample technique.
A. How to form strata?
We can say that strata can be formed on the basis of
common characteristics of the items (elements) to be put
in each stratum.
Various strata are formed in such away as to ensure
element being more homogeneous with in each stratum.
Thus, strata are purposively formed and are usually based
on past experience and personal judgment of the
researcher.
B. How should items (elements) be selected from each stratum?
The usual method for selection of items for the sample
from each stratum is that of simple random sampling.
Systematic sampling can also be used if it is considered
more appropriate in certain situation. C. How many items to be selected from each stratum (sample size)?
Stratified sample size can be made proportionate to its size
in which case the sample that is drawn from each stratum
is made proportionate to the relative size of that stratum.
E.g., suppose Pi the proportion of population included in stratum i and n represents the total sample size, the sample size of stratum i will then be pi*n
Stratified sample size can also be made
disproportionate to its size.
That is, the sample size from each stratum is
made based on other circumstance such as
based on the relative variance of stratum.
Here we take large sample size from more
variable strata (heterogeneous).
ni = n* Ni1 / N11 +N22 + N33 + …Nkk
Where
1 2, 3, …k denote the standard deviation
of the k strata,
N1 , N2, N3…Nk the size of the k strata,
ni denote the sample size of the k strata and
n the total sample size.
Generally, the procedure in Stratified sampling can be summarized as follow:
The entire population is first divided into a set of strata (sub-population groups), using some external sources, such as census data
Within each stratum a separate random sample is selected
From each separate sample, some statistics (mean) is computed and properly weighted to form an over all estimated mean for the whole population
Sample variances are also computed within each separate stratum and appropriately weighted to yield a combined estimate for the whole population.
4. Cluster sampling: This technique will sample economically while
retaining the characteristics of a probability sampling.
In cluster sampling the primary sampling unit is no more the individual elements in the population rather it is say manufacturing unit, city or block of city, etc.
After randomly selecting the primary sample unit (city, part of city), we survey or interview all families or elements in that selected primary sample unit.
The area sample is the commonly used type of cluster sampling.
E.g., suppose we want to estimate the proportion of machine-parts in an inventory, which are defective.
Assume that there are about 20000 machine parts in the inventory.
They are stored in 400 cases of each containing 50 parts each.
Now using a cluster sampling, we would consider the 400 cases as clusters.
From this cluster we randomly select say n cases and examine all the machine-parts in each randomly selected case.
Cluster sampling clearly will reduce costs
by concentrating survey in selected cluster.
But it is less precise than random
sampling.
Cluster sampling is used only because of
the economic advantage it possesses.
5. Multi-stage sampling: Items are selected in different stage at random.
Multi stage sampling is a further improvement
over cluster sampling.
E.g., If we wish to estimate say yield per hectare
of a given crop say coffee in Dessie zone. We
begin by random selection of say 5 districts in
the first instance.
Of these 5 districts, 10 villages per district will be chosen in the same manner.
In final stage we will select again randomly 5 farms from every village.
Thus, we shall examine per hectare yield in a total of 250 farms all over that region.
Zone or region District (5) first stage
Village (10 /district) second stage
Farm (5 farms/ village) third stage
There are two advantages of this sampling
technique.
It is easier to administer than most
sampling technique.
A large number of units can be sampled
for a given cost because of sequential
clustering, whereas this is not possible
in most sample design.
Multi-stage sampling is relatively
convenient, less time consuming and less
expensive method of sampling.
However, an element of sampling bias gets
introduced because of unequal size of some
of the selected sub-sample.
This method is recommended only when it
would be practical to draw a sample with a
simple random sampling technique.
II. Non-probability Sampling
It does not give equal chance that each
element of the population will be included in
the sample.
Units are selected at the discretion of the
researcher.
Such samples derive their control from the
judgment of the researcher.
Some of the disadvantages of non-probability sampling are of the following: No confidence can be placed in the data
obtained from such samples; they don't represent the large population. Therefore, the result obtained may not be
generalized for the entire population. Non-probability sampling depends
exclusively on uncontrolled factors and researcher's insight, and there is no statistical method to determine the margin of the sampling errors.
Sometimes such samples are based on an absolute
frame, which does not adequately cover the
population.
The advantages of non-probability sampling on the
other hand is that: it is much less complicated,
less expensive, and
a researcher may take the advantage of the available
respondents with out the statistical complexity of the
probability sampling.
More over it is very convenient in the situation
when the sample to be selected is very small
and the researcher wants to get some idea of the
population characteristics
Non-probability sampling can be adequate if the
researcher has no desire to generalize his findings
beyond the sample, or if the study is merely a trial
run for larger study (in preliminary research).
There are number of non-probability sampling. Quota Sampling Judgment sampling Snowball sampling Convenience sampling
1. QUOTA SAMPLING: Under this sampling approach, the interviewers are simply
given quotas to be full-filled from the different strata (groups).E.g., an interviewer in a particular city may be assigned say 100 interviews. He will assign this to different subgroups (say 50 far male respondents and 50 for female respondents).
Even though, quota sampling is not probabilistic, the researcher must take precaution to keep from biasing selection and makes sure that the sample is as representative and generalize-able as possible.
2. JUDGMENT (PURPOSIVE OR DELIBERATE) SAMPLING :
In this approach the investigator has complete freedom in choosing his sample according to his wishes and desire.
The experienced individual (researcher) select the sample based upon his judgment about some appropriate characteristics required from the sample members
The intent is to select elements that are believed to be typical or representative of the population in such a way that error of judgment in the selection will cancel each other out.
The researcher selects a sample to serve a specific purpose, even if this makes a sample less than fully representative.
The Consumers Price Index (CPI) is based on a judgment sampling. That is, based on prices of basket of goods and services purchased by average households.
The key assumption underling in this type of sampling is that, with sound judgment of expertise and an appropriate strategy, one can carefully and consciously choose the element to be included in the sample.
advantage low cost, convenient to use, less time-consuming, and as good as probability sampling.
However, its value depends on entirely on the expert judgment of the researcher.
Weakness without an objective basis for
making the judgment or without an external check, there is no way to know whether the so-called typical cases are, in-fact, typical and its value is entirely depends on the judgment of the researcher.
3. SNOWBALL SAMPLING: also known as Multiplicity sampling.
The term snowball comes from the analogy of the
snowball,
beginning small but becomes bigger and bigger as
it rolls downhill.
Snowball sampling is popular among scholars
conducting observational research and in
community study.
The major purpose of snowball sampling is
to estimate characteristics that are rare in
the total population.
First initial respondents are selected
randomly but additional respondent are then
obtained from referrals or by other
information provided by the initial
respondent.
E.g., consider a researcher use telephone to obtain referral.
Random telephone calls are made; the respondents (answering the call) are asked if they know someone else who meets the studies respondent qualification.
Like “whether they know the some one who survived the September eleven terrorist attack in New York “ SAY,
A researcher wants to study the impact of the September Eleven Terrorist attack on the social life and life style of the survivals.
advantages of this type sampling are
that:
it substantially increases the
probability of finding the desired
characteristic in the population and
lower sampling variance and cost.
4. CONVENIENCE SAMPLING : This is a "hit or miss" procedure of study. No planned effort is made to collect
information. The researcher comes across certain people
and things and has transaction with them then he tries to make generalization about the whole population.
This sampling technique is not scientific and has no value as a research technique.
However, as it is characterized by "hit or
miss" method sometimes hits are secured.
In general, the availability and willingness
to respond are the major factors in selecting
the respondents.
Commonly such a sample is taken to test
ideas or even to gain ideas about a subject of
interest.
XS XX
4.6.4. SAMPLING DISTRIBUTION
The sampling distribution is a hypothetical device that figuratively represents the distribution of a statistic (some number you’ve obtained from your sample) across an infinite number of samples.
We are often concerned with sampling
distribution in sampling analysis.
If we take certain number of samples and for
each sample compute various statistical
measures such as mean, standard deviation,
etc., then we can find that each sample may
give its own value for the statistic under
consideration.
All such values of a particular statistic, say mean, together with their relative frequencies will constitute the sampling distribution of the particular statistic, say mean.
Accordingly, we can have sampling distribution of mean, or the sampling distribution of standard deviation or the sampling distribution of any other statistical measure.
It may be noted that each item in a sampling distribution is a particular statistic of a sample.
The sampling distribution tends quite closer to the normal distribution if the number of samples is large.
The significance of sampling distribution follows from the fact that the mean of a sampling distribution is the same as the mean of the universe.
Thus, the mean of the sampling distribution can be taken as the mean of the universe.
You have to remember that your sample is just one of a potentially infinite number of samples that could have been drawn.
While it’s very likely that any statistics you generate from your sample would be near the center of the sampling distribution, just by luck of the draw, the researcher normally wants to find out exactly where the center of this sampling distribution is.
That’s because the center of the sampling distribution represents the best estimate of the population average, and the population is what you want to make inferences to.
The average of the sampling distribution is
the population parameter, and inference is all
about making generalizations from statistics
(sample) to parameters (population).
You can use some of the information you’ve
collected thus far to calculate the sampling
distribution, or more accurately, the sampling
error.
In statistics, any standard deviation of a
sampling distribution is referred to as the
standard error (to keep it separate in our
minds from standard deviation).
In sampling, the standard error is referred to
as sampling error.
SAMPLING ERROR AND NON SAMPLING ERROR :
1. SAMPLING ERROR is the difference between the result of a sample and
the result of census. is the difference between the sample estimation and
the actual value of the population. These are errors that are created because of the
chance only. Although the sample is properly selected, there will
be some difference between the sample statistics and the actual value (population parameter).
The mean of the sample might be different from the population mean by chance alone.
The standard deviation of the sample might also be different from the population standard deviation.
Therefore, we can expect some difference between the sample statistics and the population parameter.
This difference is known as sampling error.
Example,
Suppose an individual student has scored the
following grades in 10 subjects (Consider
these subjects as population); 55, 60, 65, 90,
55, 75, 88, 45, 85, 82.
Say, a sample of four grades 55, 65, 82, and
90 are selected at random from this
population to estimate the average grade of
this student.
The mean of this sample is 73.
But the population mean is 70.
The sampling error is therefore, 73 - 70 = 3.
However, the variation due to random
fluctuation (sampling error) decreases as the
sample size increases though it is not
possible to completely avoid sampling error.
2. SYSTEMATIC ERROR (NON-SAMPLING ERROR)
is also called sampling bias. Such error can be created from errors in the sampling
procedure, and it cannot be reduced or eliminate by increasing the sample
size. Such error occurs because of human mistakes and not chance
variation. The possible factors that contribute to the creation of such
error include: inappropriate sampling frame, accessibility bias, defective measuring device, and non-response bias or defects in data collection.
1. INAPPROPRIATE SAMPLING: If the sample units are a misrepresentation of the
population; it will result in sample bias.
This could happen when a researcher gathers data
from a sample that was drawn from some favored
locations.
It occurs when there is a failure of all units in the
population to have some probability of being
selected for the sample.
2. ACCESSIBILITY BIAS: In many research studies, researchers tend to
select respondents who are the most accusable to them.
When all members of the population are not equally accessible, the researcher must provide some mechanism of controlling in order to ensure the absence of over and under-representation of some respondents.
3. NON-RESPONSE BIAS: This is an incomplete coverage of sample or
inability to get complete response from all individuals initially included in the sample.
This is due to the failure in locating some of the individuals of the sample element or due to their refusal to respond.
In some cases, respondents may intentionally give false information in response to some sensitive question.
For instance, people may not tell the truth of their bad habit and income.
Maximizing accuracy requires that total study error be minimized.
Total error = Sampling error + Non-sampling Error
Total error is usually measured as total error variance, also known as mean square (MSE)
(TE) 2 = (SE) 2 + (NE) 2
Generally, non-sampling errors occur in a sample survey as well as in census survey where as the sampling error occurs only in a sample survey.
Preparing the survey questionnaire and handling the data properly can minimize non-sampling error.