Unit - Iii BRM
Unit - Iii BRM
Unit - Iii BRM
UNIT-III
Types of data – Primary Vs Secondary data – Methods of primary data collection –
Survey Vs Observation Experiments – Construction of questionnaire and
instrument – Validation of questionnaire – Sampling plan Sample size –
determinants optimal sample size – sampling techniques – Probability Vs Non–
probability sampling methods.
Secondary data
Advantages of Secondary data
Secondary data is the data that have been
already collected by someone and readily
available from other sources. Such data are 1. It is economical. It saves efforts and expenses.
cheaper and more quickly obtainable than the 2. It is time saving.
primary data and also may be available when 3. It helps to make primary data collection more
primary data cannot be obtained at all. specific since with the help of secondary data,
Secondary data collection techniques we are able to make out what are the gaps and
1) Internal secondary data deficiencies and what additional information
needs to be collected.
a) Sales records
4. It helps to improve the understanding of the
b) Marketing activity problem.
c) Cost information 5. It provides a basis for comparison for the data
d) Distribution reports and feedback that is collected by the researcher.
e) Customer feedback Disadvantages of secondary data
2) External Secondary data
Not specific to the research needs
a) Journals b) books c) magazines d) Affects research quality
newspapers e) Libraries f) Internet Incomplete information
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2. Observation
Observation: Observation is a method that employs vision as its main means of data collection.
It implies the use of eyes rather than of ears and the voice. Observation is accurate watching
and noting of phenomena (facts) as they occur with regard to the cause and effect or mutual
relations.
Observation involves recording the behavioral patterns of people, object and events in
a systematic manner to obtain information about the phenomenon of interest.
Observation is watching behaviour of other persons as it actually happens without
controlling it. Thus, recording information without asking questions is called observation
method. Following are some examples of observation method-of data collection:
Methods of Observation
1) Structured and Unstructured Observation:
Structured observation: is used when the research Advantages of observation method of data
problem has been formulated precisely and the collection
researcher is told to observe the area of study. The
researchers are asked to record their observations. 1) Subjective bias is eliminated
Unstructured observation: implies that the 2) Data is not affected by past behaviour or
researchers are free to observe whatever they feel
relevant and judicious. future intentions
2) Disguised Observation: Here, the subject or 3) Natural behaviour of the group can be
informants do not know that they are being observed.
3) Observation under Natural Setting and recorded
Laboratory Setting: Limitations of observation method
Natural setting observation: Observations in field
studies are in their natural setting and are studied in 1) Expensive methodology
extremely realistic conditions 2) Information provided is limited
Observation in a laboratory setting, on the other
hand, enables the observer to control extraneous 3) Unforeseen factors may interfere with the
variables, which influence the behaviour of people. observational task.
4) Direct and Indirect Observation
Indirect observation: Observation made by using
instruments and devices
Direct observation: is operationally defined as
an observation made by using researcher or observer
senses.
5) Human-Mechanical Observation: Here
observations are done by manually (Human) or by
machines (Mechanical).
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3. Survey Method
Survey: Survey means a planned effort to collect the desired information from a
representative sample of the relevant population generally known as respondents.
Survey research can be divided into three broad Note: Survey is one among the
categories: most popular technique, used
1) Interview Method,
2) Questionnaire Method, and in social sciences research
3) Schedule.
4) Questionnaire/Construction of Questionnaire
5. Experiments
6. SAMPLING
A finite part of a population or a subset of a set of sampling units, selected by some
process, usually by deliberate selection with the object of investigating the properties of
the parent population or set is called a sample.
For example, a fruit merchant does not examine each and every apple or mango.
He inspects only a few of them and takes decision to purchase or not to purchase. The
most important aim of sampling is to obtain maximum information about the population
under study with the least use of money, labour, and time.
1) Population or universe:
All the items, which fall within the purview of enquiry, are known as universe or
population.
For eg: (1) the population of books in the national library. (2) The population of the
heights of Indians.
2) Sample: It is a portion of the population which is examined with a view to
estimate the characteristics of the population, i.e.:
i) To assess the quality of a bag of rice, we examine only a portion of it. The portion
selected from the bag is called a sample, while the whole quantity of rice in the
bag is the population,
3) Sampling Frame: This refers to a list containing all sampling units. It generally
consists of a list of items from which the sample is to be drawn. A frame should be a
good representative of the population.
In addition to the purpose of the study and population size, three determinants usually
will need to be specified to determine the appropriate sample size:
1) Size of the Universe: The larger the size of the universe, the bigger should be.the sample
size.
2) Resources Available: If the resources available are vast a larger sample size could be taken.
However, in most cases resources constitute a big constraint on sample size.
3) Degree of Accuracy or Precision Desired: The greater the degree of accuracy desired, the
larger should be the sample size. However, it does not necessarily mean that bigger samples
always ensure greater accuracy. If a sample is selected by experts by following scientific
method, it may ensure better results even when it is small compared to a situation in which
a large sample size is selected by inexperienced people.
4) Homogeneity or Heterogeneity of the Universe: If the universe consists of homogeneous
units a small may serve the purpose but if the universe consists of heterogeneous units a
large sample may be inevitable.
5) Nature of Study: For an intensive and continuous study a small sample may be suitable.
But for studies which are not likely to be repeated and are quite extensive in nature, it may
be necessary to take a larger sample size.
6) Method of Sampling Adopted: The size of sample is also influenced by the type of
sampling plan adopted. For example, if the sample is a simple random sample it may
necessitate bigger sample size. However, in a properly drawn stratified sampling plan, even
a small sample may give better results.
7) Nature of Respondents: Where it is expected a large number of respondents will not
cooperate and send back the questionnaire, a large sample should be selected.
8) 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). 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.
9) The Confidence Level: 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.
10) Degree of Variability: The third criterion, the 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. 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
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Methods of sampling
2. Systematic Sampling: In this sampling, one unit is selected at random from the
universe and the other units are at a specified interval from the selected unit. This method
can be used when the population is finite and the units of the Universe can be arranged
on the basis of any system— like alphabetical arrangement, numerical arrangement or
geographical arrangement etc.
are distinctly different from each other but are homogeneous within. There after units are
chosen from each strata randomly.
4. Cluster Sampling: In this method, the universe is divided into some recognizable sub-
groups which are called 'clusters'. After this a simple random sample of these clusters is
drawn and then all the units belonging to the selected clusters constitute the sample. In
cluster sampling, groups of elements that are heterogeneous form a group and then the
groups are chosen randomly.
The selected first-stage samples are then sub-divided into second stage units
from which another sample is selected. Third stage and fourth-stage sampling is done in
the same manner if necessary. Thus, for an urban survey, a sample of towns may be
taken first and then for each of the selected town a sub-sample of households may be
taken, and then, if needed, from each of the selected household a third-stage-sample of
individuals may be obtained.
Area Sampling: Area Sampling is a form of multi-stage sampling in which maps, rather
than lists or registers are used as the sampling frame. It is more frequently used in those
countries which do not have a satisfactory sampling frame such as a population list.
of the man in the street, i.e., selection of units where they are. Sample units are
selected because they are accessible.
2) Judgment Sampling: A second method of non-probability sampling that is
sometimes advocated is the selection of universe items by means of expert judgment.
Using this approach, specialists in the subject matter of the survey choose what they
believe to be the best sample for that particular study.
3) Quota Sampling: One of the most commonly used non-probability sample designs
is quota sampling, which enjoys its most widespread use in consumer surveys. This
sampling method also uses the principle of stratification. As in stratified random
sampling, the researcher begins by constructing strata. Bases for stratification in
consumer surveys are commonly demographic, e.g., age, sex, income and so on.
Often compound stratification is used - for example, age groups within sex.
Next, sample sizes (called quotas) are established for each stratum. As with
stratified random sampling, the sampling within strata may be proportional or
disproportional. Field-workers are then instructed to conduct interviews with the
designated quotas, with the identification of individual respondents being left to the
field-workers.
4) Panel Sampling: Here, the initial samples are drawn on random basis and
information from these is collected on regular basis. It is a semi-permanent sample
where members may be included repetitively for successive studies. Here there is a
facility to select and quickly contact such well balanced samples and to have
relatively high response rate even by mail.