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RESEARCH METHODS FOR
MANAGEMENT – ARM1611
M. JOSEPHIN REMITHA
ASSISTANT PROFESSOR
DEPT. OF BBA, PSGRKCW
Syllabus
 UNIT – I
Meaning and Importance of Research – Methods of research – Defining research problem –
Research process.
 UNIT – II
Research Design - Formulation –Sampling and Sampling Design - Sampling Method:
Probability Sampling and Non- probability Sampling.
 UNIT – III
Data Collection – Primary and Secondary Data – Designing of Questionnaire – Interview –
Observation – Pilot Study and Case Study. Measurement and Scaling Techniques. Data
Processing: Editing, Coding, Classification and Tabulation.
Cont…
 UNIT – IV
Statistical Measures for Data Analysis: Types of Hypothesis - Formulation and testing of
Hypothesis – t-test, Chi- Square Test and one-way Anova ( Simple Problems only).
 UNIT – V
Interpretation and Report Writing – Techniques of Interpretation – Steps in Report Writing –
Layout and Types of Report. Norms for using Index, Tables, Charts, Diagram, Appendix and
Bibliography.
UNIT – II
Topics
 Research Design
 Formulation
 Sampling and Sampling Design
 Sampling Method:
 Probability Sampling and
 Non- probability Sampling.
Research Design
 Research design is the framework of research methods and techniques chosen
by a researcher. The design allows researchers to hone in on research
methods that are suitable for the subject matter and set up their studies up for
success.
 There are three main types of research design: Data collection, measurement,
and analysis.
 The type of research problem an organization is facing will determine the
research design and not vice-versa. The design phase of a study determines
which tools to use and how they are used.
Essential elements of the research design
An impactful research design usually creates a minimum bias in data and increases trust in the
accuracy of collected data. A design that produces the least margin of error in experimental research is
generally considered the desired outcome. The essential elements of the research design are:
 Accurate purpose statement
 Techniques to be implemented for collecting and analyzing research
 The method applied for analyzing collected details
 Type of research methodology
 Probable objections for research
 Settings for the research study
 Timeline
 Measurement of analysis
Key characteristics of research design
Proper research design sets your study up for success. Successful research studies provide
insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main
characteristics of a design. There are four key characteristics of research design:
 Neutrality: When you set up your study, you may have to make assumptions about the data you
expect to collect. The results projected in the research design should be free from bias and
neutral. Understand opinions about the final evaluated scores and conclusion from multiple
individuals and consider those who agree with the derived results.
 Reliability: With regularly conducted research, the researcher involved expects similar results
every time. Your design should indicate how to form research questions to ensure the standard of
results. You’ll only be able to reach the expected results if your design is reliable.
Key characteristics of research design
 Validity: There are multiple measuring tools available. However, the only
correct measuring tools are those which help a researcher in gauging results
according to the objective of the research. The questionnaire developed from
this design will then be valid.
 Generalization: The outcome of your design should apply to a population and
not just a restricted sample. A generalized design implies that your survey can
be conducted on any part of a population with similar accuracy.
The above factors affect the way respondents answer the research questions
and so all the above characteristics should be balanced in a good design.
Formulation of Research Design
 A research design is a framework or blueprint for conducting the marketing research project.
 It details the procedures necessary for obtaining the required information, and its purpose is to
design a study that will test the hypotheses of interest, determine possible answers to the
research questions, and provide the information needed for decision making.
 Decisions are also made regarding what data should be obtained from the respondents.
 A questionnaire and sampling plan also are designed in order to select the most appropriate
respondents for the study.
 The following steps are involved in formulating a research design:
Steps Involved
 Secondary data analysis (based on secondary research)
 Qualitative research
 Methods of collecting quantitative data (survey, observation, and
experimentation)
 Definition of the information needed
 Measurement and scaling procedures
 Questionnaire design
 Sampling process and sample size
 Plan of data analysis
Sampling and Sampling Design
Sampling
 Sampling is a process used in statistical analysis in which a
predetermined number of observations are taken from a larger
population.
 The methodology used to sample from a larger population depends on
the type of analysis being performed.
 When you collect any sort of data, especially quantitative data,
whether observational, through surveys or from secondary data, you
need to decide which data to collect and from whom.
 This is called the sample.
 There are a variety of ways to select your sample, and to make sure that
it gives you results that will be reliable and credible.
Principles Behind Choosing a Sample
Sample must be:
 Representative of the population. In other words, it should contain similar proportions of
subgroups as the whole population, and not exclude any particular groups, either by method
of sampling or by design, or by who chooses to respond.
 Large enough to give you enough information to avoid errors. It does not need to be a
specific proportion of your population, but it does need to be at least a certain size so that
you know that your answers are likely to be broadly correct.
If your sample is not representative, you can introduce bias into the study. If it is not large
enough, the study will be imprecise.
However, if you get the relationship between sample and population right, then you can
draw strong conclusions about the nature of the population.
 A sample design is the framework, or road map, that serves as the basis
for the selection of a survey sample and affects many other important
aspects of a survey as well.
 In a broad context, survey researchers are interested in obtaining some
type of information through a survey for some population, or universe, of
interest.
 One must define a sampling frame that represents the
population of interest, from which a sample is to be drawn.
 The sampling frame may be identical to the population, or it may be only
part of it and is therefore subject to some under coverage, or it may have
an indirect relationship to the population (e. g. the population is preschool
children and the frame is a listing of preschools). ...
Sampling Design
Defining the Population
Defining the Sample Unit
Determining the Sample Frame
Selecting a Sampling Technique
Determining the Sample Size
Execution of Sampling Process
Sampling Design Process
Sampling Techniques
Probability or Random Non-probability or Non-random
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Area Sampling
Multi Stage Sampling
Judgement Sampling
Convenience Sampling
Quota Sampling
Panel Sampling
Snowball Sampling
Probability Sampling
 Probability sampling methods allow the
researcher to be precise about the
relationship between the sample and the
population.
 This means that you can be absolutely confident
about whether your sample is representative or
not, and you can also put a number on how
certain you are about your findings
Simple Random
 In simple random sampling, every member of the population
has an equal chance of being chosen. The drawback is that the
sample may not be genuinely representative. Small but important
sub-sections of the population may not be included.
Advantages
 Simplicity
 Requires little prior knowledge of the population
Disadvantages
 Lower accuracy
 Higher cost
 Lower efficiency
 Samples may be clustered spatially
 Samples may not be representative of the feature attribute(s)
Procedure of Simple Random Sampling
Simple Random
Lottery Method
Random
Number Tables
Lottery Method
 The method of lottery is the most primitive and mechanical example of random sampling.
 In this method you will have to number each member of population in a consequent manner,
writing numbers in separate pieces of paper. These pieces of papers are to be folded and
mixed into a box. Lastly, samples are to be taken randomly from the box by choosing folded
pieces of papers in a random manner.
 Lottery method suffers from few drawbacks. The process of writing N number of slips is
cumbersome and shuffling a large number of slips, where population size is very large, is
difficult. Also human bias may enter while choosing the slips. Hence the other alternative i.e.
random numbers can be used.
Random Number Tables Method
 These consist of columns of numbers which have been randomly prepared. Number of random tables are
available e.g. Fisher and Yates Tables, Tippets random number etc. Listed below is a sequence of two digit
random numbers from Fisher & Yates table:
 61, 44, 65, 22, 01, 67, 76, 23, 57, 58, 54, 11, 33, 86, 07, 26, 75,76, 64, 22, 19, 35, 74, 49, 86, 58, 69, 52, 27, 34,
91, 25, 34, 67, 76,73, 27, 16, 53, 18, 19, 69, 32, 52, 38, 72, 38, 64, 81, 79 and38.
 The first step involves assigning a unique number to each member of the population e.g. if the population
comprises of 20 people then all individuals are numbered from 01 to 20. If we are to collect a sample of 5 units
then referring to the random number tables 5 double digit numbers are chosen. E.g. using the above table the
units having the following five numbers will form a sample: 01, 11, 07, 19 and 16. If the sampling is without
replacement and a particular random number repeats itself then it will not be taken again and the next number
that fits our criteria will be chosen.
Systematic random
Systematic random sampling relies on having a list of the population, which should ideally be
randomly ordered. The researcher then takes every nth name from the list.
Advantages
 There is no need to assign a unique number to each element.
 It is statistically more efficient if the population elements have
similar characteristics.
Disadvantages
 “Periodicity” in population that coincides with the sampling ratio, then the randomness is
lost.
 There is a “monotonic trend” in population i.e. The sampling frame has been arranged in some
order like a chronological order or from smallest to largest etc.
Stratified Random
 An alternative method called stratified random sampling. This method
divides the population into smaller homogeneous groups, called strata, and
then takes a random sample from each stratum.
 Ex- Understudies of school can be separated into strata on the premise of
sexual orientation, courses offered, age and so forth. In this the population
is initially partitioned into strata and afterward a basic irregular specimen is
taken from every stratum.
Types of Stratified Sampling
Stratified
Sampling
Proportionate
Stratified
Sampling
Disproportionate
Stratified
Sampling
Proportionate Stratified Sampling
 In this the number of units selected from each stratum is proportionate to
the share of stratum in the population.
 Ex- In a college there are total 2500 students out of which 1500 students
are enrolled in graduate courses and 1000 are enrolled in post graduate
courses. If a sample of 100 is to be chosen using proportionate stratified
sampling then the number of undergraduate students in sample would be
60 and 40 would be post graduate students. Thus the two strata are
represented in the same proportion in the sample as is their representation
in the population.
 This method is most suitable when the purpose of sampling is to estimate
the population value of some characteristic and there is no difference in
within- stratum variances.
Disproportionate Stratified Sampling
 In disproportionate stratified random sampling, the different
strata do not have the same sampling fractions as each other.
 For instance, if your four strata contain 200, 400, 600, and 800 people,
you may choose to have different sampling fractions for each stratum.
 Perhaps the first stratum with 200 people has a sampling fraction of ½,
resulting in 100 people selected for the sample, while the last stratum with
800 people has a sampling fraction of ¼, resulting in 200 people selected for
the sample.
 The precision of using disproportionate stratified random sampling is highly
dependent on the sampling fractions chosen and used by the researcher.
 Here, the researcher must be very careful and know exactly what they are
doing. Mistakes made in choosing and using sampling fractions could result
in a stratum that is over- represented or under-represented, resulting in
skewed results.
Advantages of Stratified Sampling
 Stratified random sampling is superior to simple random
sampling because the process of stratifying reduces sampling
error and ensures a greater level of representation.
 Thanks to the choice of stratified random sampling adequate
representation of all subgroups can be ensured.
 When there is homogeneity within strata and heterogeneity
between strata, the estimates can be as precise (or even more
precise) as with the use of simple random sampling.
Disadvantages of Stratified Sampling
 The application of stratified random sampling requires the knowledge of strata
membership a priori. The requirement to be able to easily distinguish between
strata in the sample frame may create difficulties in practical levels.
 Research process may take longer and prove to be more expensive due to the
extra stage in the sampling procedure
 The choice of stratified sampling method adds certain complexity to the analysis
plan
Cluster sampling
 Cluster sampling is used in statistics when natural groups are
present in a population.
 Designed to address problems of a widespread geographical
population. Random sampling from a large population is likely to lead
to high costs of access. This can be overcome by dividing the
population into clusters, selecting only two or three clusters, and
sampling from within those. For example, if you wished to find out
about the use of transport in urban areas in the UK, you could
randomly select just two or three cities, and then sample fully from
within these.
Difference Between Cluster Sampling and Stratified Sampling
 For a stratified random sample, a population is divided into stratum, or
sub-populations, before sampling.
 At first glance, the two techniques seem very similar.
However, in cluster sampling the actual cluster is
the sampling unit; in stratified sampling, analysis is done on elements
within each strata.
 In cluster sampling, a researcher will only study selected clusters;
with stratified sampling, a random sample is drawn from each strata.
Area Sampling
 Area sampling involves sampling from a map, an aerial photograph, or a similar
area frame. It is often the sampling method of choice when a sampling frame isn’t
available.
 For example, a city map can be divided into equal size blocks, from which random
samples can be drawn. Although area sampling is most often associated with maps.
 Clusters and Sub sampling
 The samples drawn from an area frame are often referred to as clusters. These
clusters may be sub sampled several more times.
 For example, let’s say you wanted to sample from a population of middle school
students. The first sample might be drawn from a list of school districts, the second
sample from a list of schools, the third a list of classes and then finally a list of
students within those classes. The “frame” in this example is the four successive
layers.
Area Sampling
Advantages
 Area frames can be used for multiple variables at the same time. For example, an area
sample on a city can collect data on land use, population and income statistics.
 There’s no overlap between sampling units; Every unit has an equal chance of being
selected. This complete coverage results in unbiased estimates.
Disadvantages
 Although the area frames can be used in subsequent surveys, they can quickly become
outdated (for example, if a city undergoes tremendous growth).
 Area frames can be costly to build.
 Outliers can be a problem, especially if your map has a few particularly dense or sparse
areas (for example a city that has a national park in its boundaries might have zero
population in some areas and a huge population in another.
Multistage Sampling
 Multi-stage sampling (also known as multi-stage cluster sampling) is a
more complex form of cluster sampling which contains two or more stages
in sample selection.
 A combination of stratified sampling or cluster sampling and simple
random sampling is usually used.
Advantages of Multi-Stage Sampling
 Effective in primary data collection from geographically dispersed.
population when face-to-face contact in required (e.g. semi-structured in-
depth interviews)
 Cost-effectiveness and time-effectiveness.
 High level of flexibility.
Disadvantages of Multi-Stage Sampling
 High level of subjectivity.
 Research findings can never be 100% representative of
population.
 The presence of group-level information is required.
Non-Probability Sampling
 Non-probability sampling is a sampling technique where the odds of any member
being selected for a sample cannot be calculated.
 It’s the opposite of probability sampling, where you can calculate the odds. In
addition, probability sampling involves random selection, while non-probability
sampling does not—it relies on the subjective judgement of the researcher.
 The odds do not have to be equal for a method to be considered probability
sampling. For example, one person could have a 10% chance of being selected
and another person could have a 50% chance of being selected.
 It’s non-probability sampling when you can’t calculate the odds at all.
Convenience sampling
 Although convenience sampling is, like the name suggests—convenient—it runs a high risk that
your sample will not represent the population.
 However, sometimes a convenience sample is the only way you can drum up participants.
 According to Barbara Sommer at UC Davis, it could be “…a matter of taking what you can get”.
 Convenience sampling does have its uses, especially when you need to conduct a study quickly or
you are on a shoestring budget.
 It is also one of the only methods you can use when you can’t get a list of all the members of
a population.
For example, let’s say you were conducting a survey for a company who wanted to know what
Walmart employees think of their wages. It’s unlikely you’ll be able to get a list of employees, so you may
have to resort to standing outside of Walmart and grabbing whichever employees come out of the door
(hence the name “grab sampling”).
Haphazard sampling
 Haphazard sampling is where you try to create a random sample by haphazardly
choosing items in order to try and recreate true randomness.
 It doesn’t usually work, because of selection bias: where you knowingly or
unknowingly create unrepresentative samples.
 In order to create a true random selection, you need to use one of the tried and
testing random selection methods, like simple random sampling.
Purposive sample
 A purposive sample is where a researcher selects a sample based on their knowledge about the
study and population.
 The participants are selected based on the purpose of the sample, hence the name.
 Participants are selected according to the needs of the study (hence the alternate
name, deliberate sampling); applicants who do not meet the profile are rejected.
 For example, you may be conducting a study on why high school students choose community
college over university.
 You might canvas high school students and your first question would be “Are you planning to
attend college?” People who answer “No,” would be excluded from the study.
Expert sampling
 Expert sampling (or judgment sampling) is where you draw your sample from experts in the field you’re
studying.
 It’s used when you need the opinions or assessment of people with a high degree of knowledge about
the study area.
 When used in this way, expert sampling is a simple sub-type of purposive sampling.
 A second reason to use experts is to validate another sampling method (Singh, 2007).
For example, let’s say you want to use snowball sampling to identify addicts in your area. You are
concerned that using this non-random sampling method will adversely affect your results and the way your
results are perceived by others. You can ask a panel of experts their opinion on whether snowball
sampling is the most appropriate sampling method.
Heterogeneity
 Heterogeneity in statistics means that your populations, samples or results are different.
 It is the opposite of homogeneity, which means that the population/data/results are the same.
 A heterogeneous population or sample is one where every member has a different value for the
characteristic you’re interested in.
For example, if everyone in your group varied between 4’3″ and 7’6″ tall, they would be heterogeneous
for height. In real life, heterogeneous populations are extremely common. For example, patients are
typically a very heterogeneous population as they differ with many factors including demographics,
diagnostic test results, and medical histories.
Modal instance sampling
 The purpose of modal instance sampling is to sample the most typical members
of a population.
 The term modal comes from the mode, which is the most common item in a set.
 As modal instance sampling is very difficult to implement fairly, it is only
recommended as a method for informal questionnaires or surveys.
For example, newscasters might interview a typical voter, or a typical resident, or
even residents of a typical neighborhood.
Quota sampling
 Quota sampling means to take a very tailored sample that’s in proportion to some characteristic
or trait of a population.
For example, you could divide a population by the state they live in, income or education level,
or sex. The population is divided into groups (also called strata) and samples are taken from each
group to meet a quota. Care is taken to maintain the correct proportions representative of the
population. For example, if your population consists of 45% female and 55% male,
your sample should reflect those percentages. Quota sampling is based on the researcher’s
judgment and is considered a non-probability sampling technique.
Snowball sampling
 Snowball sampling is where research participants recruit other participants for a test or study. It
is used where potential participants are hard to find.
 It’s called snowball sampling because (in theory) once you have the ball rolling, it picks up
more “snow” along the way and becomes larger and larger.
 Snowball sampling is a non-probability sampling method. It doesn’t have
the probability involved, with say, simple random sampling (where the odds are the same for
any particular participant being chosen).
 Rather, the researchers used their own judgment to choose participants.
Thank You

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Research Methodology - Research Design & Sample Design

  • 1. RESEARCH METHODS FOR MANAGEMENT – ARM1611 M. JOSEPHIN REMITHA ASSISTANT PROFESSOR DEPT. OF BBA, PSGRKCW
  • 2. Syllabus  UNIT – I Meaning and Importance of Research – Methods of research – Defining research problem – Research process.  UNIT – II Research Design - Formulation –Sampling and Sampling Design - Sampling Method: Probability Sampling and Non- probability Sampling.  UNIT – III Data Collection – Primary and Secondary Data – Designing of Questionnaire – Interview – Observation – Pilot Study and Case Study. Measurement and Scaling Techniques. Data Processing: Editing, Coding, Classification and Tabulation.
  • 3. Cont…  UNIT – IV Statistical Measures for Data Analysis: Types of Hypothesis - Formulation and testing of Hypothesis – t-test, Chi- Square Test and one-way Anova ( Simple Problems only).  UNIT – V Interpretation and Report Writing – Techniques of Interpretation – Steps in Report Writing – Layout and Types of Report. Norms for using Index, Tables, Charts, Diagram, Appendix and Bibliography.
  • 4. UNIT – II Topics  Research Design  Formulation  Sampling and Sampling Design  Sampling Method:  Probability Sampling and  Non- probability Sampling.
  • 5. Research Design  Research design is the framework of research methods and techniques chosen by a researcher. The design allows researchers to hone in on research methods that are suitable for the subject matter and set up their studies up for success.  There are three main types of research design: Data collection, measurement, and analysis.  The type of research problem an organization is facing will determine the research design and not vice-versa. The design phase of a study determines which tools to use and how they are used.
  • 6. Essential elements of the research design An impactful research design usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the least margin of error in experimental research is generally considered the desired outcome. The essential elements of the research design are:  Accurate purpose statement  Techniques to be implemented for collecting and analyzing research  The method applied for analyzing collected details  Type of research methodology  Probable objections for research  Settings for the research study  Timeline  Measurement of analysis
  • 7. Key characteristics of research design Proper research design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics of research design:  Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research design should be free from bias and neutral. Understand opinions about the final evaluated scores and conclusion from multiple individuals and consider those who agree with the derived results.  Reliability: With regularly conducted research, the researcher involved expects similar results every time. Your design should indicate how to form research questions to ensure the standard of results. You’ll only be able to reach the expected results if your design is reliable.
  • 8. Key characteristics of research design  Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The questionnaire developed from this design will then be valid.  Generalization: The outcome of your design should apply to a population and not just a restricted sample. A generalized design implies that your survey can be conducted on any part of a population with similar accuracy. The above factors affect the way respondents answer the research questions and so all the above characteristics should be balanced in a good design.
  • 9. Formulation of Research Design  A research design is a framework or blueprint for conducting the marketing research project.  It details the procedures necessary for obtaining the required information, and its purpose is to design a study that will test the hypotheses of interest, determine possible answers to the research questions, and provide the information needed for decision making.  Decisions are also made regarding what data should be obtained from the respondents.  A questionnaire and sampling plan also are designed in order to select the most appropriate respondents for the study.  The following steps are involved in formulating a research design:
  • 10. Steps Involved  Secondary data analysis (based on secondary research)  Qualitative research  Methods of collecting quantitative data (survey, observation, and experimentation)  Definition of the information needed  Measurement and scaling procedures  Questionnaire design  Sampling process and sample size  Plan of data analysis
  • 12. Sampling  Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population.  The methodology used to sample from a larger population depends on the type of analysis being performed.  When you collect any sort of data, especially quantitative data, whether observational, through surveys or from secondary data, you need to decide which data to collect and from whom.  This is called the sample.  There are a variety of ways to select your sample, and to make sure that it gives you results that will be reliable and credible.
  • 13. Principles Behind Choosing a Sample Sample must be:  Representative of the population. In other words, it should contain similar proportions of subgroups as the whole population, and not exclude any particular groups, either by method of sampling or by design, or by who chooses to respond.  Large enough to give you enough information to avoid errors. It does not need to be a specific proportion of your population, but it does need to be at least a certain size so that you know that your answers are likely to be broadly correct. If your sample is not representative, you can introduce bias into the study. If it is not large enough, the study will be imprecise. However, if you get the relationship between sample and population right, then you can draw strong conclusions about the nature of the population.
  • 14.  A sample design is the framework, or road map, that serves as the basis for the selection of a survey sample and affects many other important aspects of a survey as well.  In a broad context, survey researchers are interested in obtaining some type of information through a survey for some population, or universe, of interest.  One must define a sampling frame that represents the population of interest, from which a sample is to be drawn.  The sampling frame may be identical to the population, or it may be only part of it and is therefore subject to some under coverage, or it may have an indirect relationship to the population (e. g. the population is preschool children and the frame is a listing of preschools). ... Sampling Design
  • 15. Defining the Population Defining the Sample Unit Determining the Sample Frame Selecting a Sampling Technique Determining the Sample Size Execution of Sampling Process Sampling Design Process
  • 16. Sampling Techniques Probability or Random Non-probability or Non-random Simple Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling Area Sampling Multi Stage Sampling Judgement Sampling Convenience Sampling Quota Sampling Panel Sampling Snowball Sampling
  • 17. Probability Sampling  Probability sampling methods allow the researcher to be precise about the relationship between the sample and the population.  This means that you can be absolutely confident about whether your sample is representative or not, and you can also put a number on how certain you are about your findings
  • 18. Simple Random  In simple random sampling, every member of the population has an equal chance of being chosen. The drawback is that the sample may not be genuinely representative. Small but important sub-sections of the population may not be included. Advantages  Simplicity  Requires little prior knowledge of the population Disadvantages  Lower accuracy  Higher cost  Lower efficiency  Samples may be clustered spatially  Samples may not be representative of the feature attribute(s)
  • 19. Procedure of Simple Random Sampling Simple Random Lottery Method Random Number Tables
  • 20. Lottery Method  The method of lottery is the most primitive and mechanical example of random sampling.  In this method you will have to number each member of population in a consequent manner, writing numbers in separate pieces of paper. These pieces of papers are to be folded and mixed into a box. Lastly, samples are to be taken randomly from the box by choosing folded pieces of papers in a random manner.  Lottery method suffers from few drawbacks. The process of writing N number of slips is cumbersome and shuffling a large number of slips, where population size is very large, is difficult. Also human bias may enter while choosing the slips. Hence the other alternative i.e. random numbers can be used.
  • 21. Random Number Tables Method  These consist of columns of numbers which have been randomly prepared. Number of random tables are available e.g. Fisher and Yates Tables, Tippets random number etc. Listed below is a sequence of two digit random numbers from Fisher & Yates table:  61, 44, 65, 22, 01, 67, 76, 23, 57, 58, 54, 11, 33, 86, 07, 26, 75,76, 64, 22, 19, 35, 74, 49, 86, 58, 69, 52, 27, 34, 91, 25, 34, 67, 76,73, 27, 16, 53, 18, 19, 69, 32, 52, 38, 72, 38, 64, 81, 79 and38.  The first step involves assigning a unique number to each member of the population e.g. if the population comprises of 20 people then all individuals are numbered from 01 to 20. If we are to collect a sample of 5 units then referring to the random number tables 5 double digit numbers are chosen. E.g. using the above table the units having the following five numbers will form a sample: 01, 11, 07, 19 and 16. If the sampling is without replacement and a particular random number repeats itself then it will not be taken again and the next number that fits our criteria will be chosen.
  • 22. Systematic random Systematic random sampling relies on having a list of the population, which should ideally be randomly ordered. The researcher then takes every nth name from the list. Advantages  There is no need to assign a unique number to each element.  It is statistically more efficient if the population elements have similar characteristics. Disadvantages  “Periodicity” in population that coincides with the sampling ratio, then the randomness is lost.  There is a “monotonic trend” in population i.e. The sampling frame has been arranged in some order like a chronological order or from smallest to largest etc.
  • 23. Stratified Random  An alternative method called stratified random sampling. This method divides the population into smaller homogeneous groups, called strata, and then takes a random sample from each stratum.  Ex- Understudies of school can be separated into strata on the premise of sexual orientation, courses offered, age and so forth. In this the population is initially partitioned into strata and afterward a basic irregular specimen is taken from every stratum.
  • 24. Types of Stratified Sampling Stratified Sampling Proportionate Stratified Sampling Disproportionate Stratified Sampling
  • 25. Proportionate Stratified Sampling  In this the number of units selected from each stratum is proportionate to the share of stratum in the population.  Ex- In a college there are total 2500 students out of which 1500 students are enrolled in graduate courses and 1000 are enrolled in post graduate courses. If a sample of 100 is to be chosen using proportionate stratified sampling then the number of undergraduate students in sample would be 60 and 40 would be post graduate students. Thus the two strata are represented in the same proportion in the sample as is their representation in the population.  This method is most suitable when the purpose of sampling is to estimate the population value of some characteristic and there is no difference in within- stratum variances.
  • 26. Disproportionate Stratified Sampling  In disproportionate stratified random sampling, the different strata do not have the same sampling fractions as each other.  For instance, if your four strata contain 200, 400, 600, and 800 people, you may choose to have different sampling fractions for each stratum.  Perhaps the first stratum with 200 people has a sampling fraction of ½, resulting in 100 people selected for the sample, while the last stratum with 800 people has a sampling fraction of ¼, resulting in 200 people selected for the sample.  The precision of using disproportionate stratified random sampling is highly dependent on the sampling fractions chosen and used by the researcher.  Here, the researcher must be very careful and know exactly what they are doing. Mistakes made in choosing and using sampling fractions could result in a stratum that is over- represented or under-represented, resulting in skewed results.
  • 27. Advantages of Stratified Sampling  Stratified random sampling is superior to simple random sampling because the process of stratifying reduces sampling error and ensures a greater level of representation.  Thanks to the choice of stratified random sampling adequate representation of all subgroups can be ensured.  When there is homogeneity within strata and heterogeneity between strata, the estimates can be as precise (or even more precise) as with the use of simple random sampling.
  • 28. Disadvantages of Stratified Sampling  The application of stratified random sampling requires the knowledge of strata membership a priori. The requirement to be able to easily distinguish between strata in the sample frame may create difficulties in practical levels.  Research process may take longer and prove to be more expensive due to the extra stage in the sampling procedure  The choice of stratified sampling method adds certain complexity to the analysis plan
  • 29. Cluster sampling  Cluster sampling is used in statistics when natural groups are present in a population.  Designed to address problems of a widespread geographical population. Random sampling from a large population is likely to lead to high costs of access. This can be overcome by dividing the population into clusters, selecting only two or three clusters, and sampling from within those. For example, if you wished to find out about the use of transport in urban areas in the UK, you could randomly select just two or three cities, and then sample fully from within these.
  • 30. Difference Between Cluster Sampling and Stratified Sampling  For a stratified random sample, a population is divided into stratum, or sub-populations, before sampling.  At first glance, the two techniques seem very similar. However, in cluster sampling the actual cluster is the sampling unit; in stratified sampling, analysis is done on elements within each strata.  In cluster sampling, a researcher will only study selected clusters; with stratified sampling, a random sample is drawn from each strata.
  • 31. Area Sampling  Area sampling involves sampling from a map, an aerial photograph, or a similar area frame. It is often the sampling method of choice when a sampling frame isn’t available.  For example, a city map can be divided into equal size blocks, from which random samples can be drawn. Although area sampling is most often associated with maps.  Clusters and Sub sampling  The samples drawn from an area frame are often referred to as clusters. These clusters may be sub sampled several more times.  For example, let’s say you wanted to sample from a population of middle school students. The first sample might be drawn from a list of school districts, the second sample from a list of schools, the third a list of classes and then finally a list of students within those classes. The “frame” in this example is the four successive layers.
  • 32. Area Sampling Advantages  Area frames can be used for multiple variables at the same time. For example, an area sample on a city can collect data on land use, population and income statistics.  There’s no overlap between sampling units; Every unit has an equal chance of being selected. This complete coverage results in unbiased estimates. Disadvantages  Although the area frames can be used in subsequent surveys, they can quickly become outdated (for example, if a city undergoes tremendous growth).  Area frames can be costly to build.  Outliers can be a problem, especially if your map has a few particularly dense or sparse areas (for example a city that has a national park in its boundaries might have zero population in some areas and a huge population in another.
  • 33. Multistage Sampling  Multi-stage sampling (also known as multi-stage cluster sampling) is a more complex form of cluster sampling which contains two or more stages in sample selection.  A combination of stratified sampling or cluster sampling and simple random sampling is usually used. Advantages of Multi-Stage Sampling  Effective in primary data collection from geographically dispersed. population when face-to-face contact in required (e.g. semi-structured in- depth interviews)  Cost-effectiveness and time-effectiveness.  High level of flexibility. Disadvantages of Multi-Stage Sampling  High level of subjectivity.  Research findings can never be 100% representative of population.  The presence of group-level information is required.
  • 34. Non-Probability Sampling  Non-probability sampling is a sampling technique where the odds of any member being selected for a sample cannot be calculated.  It’s the opposite of probability sampling, where you can calculate the odds. In addition, probability sampling involves random selection, while non-probability sampling does not—it relies on the subjective judgement of the researcher.  The odds do not have to be equal for a method to be considered probability sampling. For example, one person could have a 10% chance of being selected and another person could have a 50% chance of being selected.  It’s non-probability sampling when you can’t calculate the odds at all.
  • 35. Convenience sampling  Although convenience sampling is, like the name suggests—convenient—it runs a high risk that your sample will not represent the population.  However, sometimes a convenience sample is the only way you can drum up participants.  According to Barbara Sommer at UC Davis, it could be “…a matter of taking what you can get”.  Convenience sampling does have its uses, especially when you need to conduct a study quickly or you are on a shoestring budget.  It is also one of the only methods you can use when you can’t get a list of all the members of a population. For example, let’s say you were conducting a survey for a company who wanted to know what Walmart employees think of their wages. It’s unlikely you’ll be able to get a list of employees, so you may have to resort to standing outside of Walmart and grabbing whichever employees come out of the door (hence the name “grab sampling”).
  • 36. Haphazard sampling  Haphazard sampling is where you try to create a random sample by haphazardly choosing items in order to try and recreate true randomness.  It doesn’t usually work, because of selection bias: where you knowingly or unknowingly create unrepresentative samples.  In order to create a true random selection, you need to use one of the tried and testing random selection methods, like simple random sampling.
  • 37. Purposive sample  A purposive sample is where a researcher selects a sample based on their knowledge about the study and population.  The participants are selected based on the purpose of the sample, hence the name.  Participants are selected according to the needs of the study (hence the alternate name, deliberate sampling); applicants who do not meet the profile are rejected.  For example, you may be conducting a study on why high school students choose community college over university.  You might canvas high school students and your first question would be “Are you planning to attend college?” People who answer “No,” would be excluded from the study.
  • 38. Expert sampling  Expert sampling (or judgment sampling) is where you draw your sample from experts in the field you’re studying.  It’s used when you need the opinions or assessment of people with a high degree of knowledge about the study area.  When used in this way, expert sampling is a simple sub-type of purposive sampling.  A second reason to use experts is to validate another sampling method (Singh, 2007). For example, let’s say you want to use snowball sampling to identify addicts in your area. You are concerned that using this non-random sampling method will adversely affect your results and the way your results are perceived by others. You can ask a panel of experts their opinion on whether snowball sampling is the most appropriate sampling method.
  • 39. Heterogeneity  Heterogeneity in statistics means that your populations, samples or results are different.  It is the opposite of homogeneity, which means that the population/data/results are the same.  A heterogeneous population or sample is one where every member has a different value for the characteristic you’re interested in. For example, if everyone in your group varied between 4’3″ and 7’6″ tall, they would be heterogeneous for height. In real life, heterogeneous populations are extremely common. For example, patients are typically a very heterogeneous population as they differ with many factors including demographics, diagnostic test results, and medical histories.
  • 40. Modal instance sampling  The purpose of modal instance sampling is to sample the most typical members of a population.  The term modal comes from the mode, which is the most common item in a set.  As modal instance sampling is very difficult to implement fairly, it is only recommended as a method for informal questionnaires or surveys. For example, newscasters might interview a typical voter, or a typical resident, or even residents of a typical neighborhood.
  • 41. Quota sampling  Quota sampling means to take a very tailored sample that’s in proportion to some characteristic or trait of a population. For example, you could divide a population by the state they live in, income or education level, or sex. The population is divided into groups (also called strata) and samples are taken from each group to meet a quota. Care is taken to maintain the correct proportions representative of the population. For example, if your population consists of 45% female and 55% male, your sample should reflect those percentages. Quota sampling is based on the researcher’s judgment and is considered a non-probability sampling technique.
  • 42. Snowball sampling  Snowball sampling is where research participants recruit other participants for a test or study. It is used where potential participants are hard to find.  It’s called snowball sampling because (in theory) once you have the ball rolling, it picks up more “snow” along the way and becomes larger and larger.  Snowball sampling is a non-probability sampling method. It doesn’t have the probability involved, with say, simple random sampling (where the odds are the same for any particular participant being chosen).  Rather, the researchers used their own judgment to choose participants.