Statistics

1. 2-1
Chapter 2
Sampling and Data
Collection

2. 2-2
Basic Concepts in Samples and Sampling
• Population: the entire group under study as
defined by research objectives. Sometimes
called the “universe.”
Researchers define populations in specific terms
such as heads of households, individual person
types, families, types of retail outlets, etc.
Population geographic location and time of study
are also considered.

3. 2-3
Basic Concepts in Samples and Sampling
• Sample: a subset of the population that should
represent the entire group
• Sample unit: the basic level of
investigation…consumers, store managers, shelf-
facings, teens, etc. The research objective
should define the sample unit
• Census: an accounting of the complete
population

4. 2-4
Reasons for Taking a Sample
• Practical considerations such as cost and
population size
• Inability of researcher to analyze large quantities
of data potentially generated by a census
• Samples can produce sound results if proper
rules are followed for the draw

5. 2-5
Basic Sampling Classifications
• Probability samples: ones in which members of
the population have a known chance (probability)
of being selected
• Non-probability samples: instances in which the
chances (probability) of selecting members from
the population are unknown

6. 2-6
Probability Sampling Methods
Simple Random Sampling
• Simple random sampling: the probability of being
selected is “known and equal” for all members of the
population
• Blind Draw Method (e.g. names “placed in a hat”
and then drawn randomly)
• Random Numbers Method (all items in the
sampling frame given numbers, numbers then
drawn using table or computer program)
• Advantages:
• Known and equal chance of selection
• Easy method when there is an electronic database

7. 2-7
Probability Sampling Methods
Simple Random Sampling
• Disadvantages: (Overcome with electronic database)
• Complete accounting of population needed
• Cumbersome to provide unique designations to
every population member
• Very inefficient when applied to skewed population
distribution (over- and under-sampling problems) –
this is not “overcome with the use of an electronic
database)

8. 2-8

9. 2-9
Probability Sampling Methods
Systematic Sampling (A Cluster Method)
• Systematic sampling: way to select a probability-
based sample from a directory or list. This
method is at times more efficient than simple
random sampling. This is a type of cluster
sampling method.
• Sampling interval (SI) = population list size (N)
divided by a pre-determined sample size (n)
• How to draw: 1) calculate SI, 2) select a
number between 1 and SI randomly, 3) go to
this number as the starting point and the item
on the list here is the first in the sample, 4) add
SI to the position number of this item and the
new position will be the second sampled item,
5) continue this process until desired sample
size is reached.

10. 2-10
Probability Sampling Methods
Systematic Sampling
• Advantages:
• Known and equal chance of any of the SI
“clusters” being selected
• Efficiency..do not need to designate (assign a
number to) every population member, just
those early on on the list (unless there is a
very large sampling frame).
• Less expensive…faster than SRS
• Disadvantages:
• Small loss in sampling precision
• Potential “periodicity” problems

11. 2-11

12. 2-12
Probability Sampling Methods
Cluster Sampling
• Cluster sampling: method by which the
population is divided into groups (clusters), any
of which can be considered a representative
sample. These clusters are mini-populations and
therefore are heterogeneous. Once clusters are
established a random draw is done to select one
(or more) clusters to represent the population.
Area and systematic sampling (discussed earlier)
are two common methods.
• Area sampling

13. 2-13
Probability Sampling Methods
Cluster Sampling
• Advantages
• Economic efficiency … faster and less
expensive than SRS
• Does not require a list of all members of the
universe
• Disadvantage:
• Cluster specification error…the more
homogeneous the cluster chosen, the more
imprecise the sample results

14. 2-14
Probability Sampling Methods
Cluster Sampling – Area Method
• Drawing the area sample:
• Divide the geo area into sectors (subareas)
and give them names/numbers, determine how
many sectors are to be sampled (typically a
judgment call), randomly select these
subareas. Do either a census or a systematic
draw within each area.
• To determine the total geo area estimate add
the counts in the subareas together and
multiply this number by the ratio of the total
number of subareas divided by number of
subareas.

15. 2-15

16. 2-16
A two-step area cluster sample (sampling several clusters) is
preferable to a one-step (selecting only one cluster) sample
unless the clusters are homogeneous

17. 2-17
Probability Sampling Methods
Stratified Sampling Method
 This method is used when the population
distribution of items is skewed. It allows us to
draw a more representative sample. Hence if
there are more of certain type of item in the
population the sample has more of this type
and if there are fewer of another type, there are
fewer in the sample.

18. 2-18
Probability Sampling Methods
Stratified Sampling
• Stratified sampling: the population is separated
into homogeneous groups/segments/strata and a
sample is taken from each. The results are then
combined to get the picture of the total
population.
• Sample stratum size determination
• Proportional method (stratum share of total
sample is stratum share of total population)
• Disproportionate method (variances among
strata affect sample size for each stratum)

19. 2-19
Probability Sampling Methods
Stratified Sampling
• Advantage:
• More accurate overall sample of skewed
population…see next slide for WHY
• Disadvantage:
• More complex sampling plan requiring
different sample sizes for each stratum

20. 2-20
Why is Stratified Sampling more accurate when
there are skewed populations?
The less the variance in a group, the smaller the
sample size it takes to produce a precise
answer.
Why? If 99% of the population (low variance)
agreed on the choice of brand A, it would be
easy to make a precise estimate that the
population preferred brand A even with a small
sample size.
But, if 33% chose brand A, and 23% chose B,
and so on (high variance) it would be difficult to
make a precise estimate of the population’s
preferred brand…it would take a larger sample

21. 2-21
Why is Stratified Sampling more accurate when
there are skewed populations? Continued..
Stratified sampling allows the researcher to
allocate a larger sample size to strata
with more variance and smaller sample
size to strata with less variance. Thus,
for the same sample size, more precision
is achieved.
This is normally accomplished by
disproportionate sampling.

22. 2-22

23. 2-23
Nonprobability Sampling Methods
Convenience Sampling Method
• Convenience samples: samples drawn at the
convenience of the interviewer. People tend to
make the selection at familiar locations and to
choose respondents who are like themselves.
• Error occurs 1) in the form of members of the
population who are infrequent or nonusers of
that location and 2) who are not typical in the
population

24. 2-24
Nonprobability Sampling Methods
Judgment Sampling Method
• Judgment samples: samples that require a
judgment or an “educated guess” on the part of
the interviewer as to who should represent the
population. Also, “judges” (informed individuals)
may be asked to suggest who should be in the
sample.
• Subjectivity enters in here, and certain
members of the population will have a smaller
or no chance of selection compared to others

25. 2-25
Nonprobabilty Sampling Methods
Referral and Quota Sampling Methods
• Referral samples (snowball samples): samples which
require respondents to provide the names of
additional respondents
• Members of the population who are less known,
disliked, or whose opinions conflict with the
respondent have a low probability of being
selected.
• Quota samples: samples that set a specific number
of certain types of individuals to be interviewed
• Often used to ensure that convenience samples
will have desired proportion of different
respondent classes

26. 2-26
Online Sampling Techniques
• Random online intercept sampling: relies on a
random selection of Web site visitors
• Invitation online sampling: is when potential
respondents are alerted that they may fill out a
questionnaire that is hosted at a specific Web site
• Online panel sampling: refers to consumer or
other respondent panels that are set up by
marketing research companies for the explicit
purpose of conducting online surveys with
representative samples

27. 2-27
Sayanti Banerjee
Methods and Techniques
of Data Collection

28. 2-28
Classification of Data
1. Primary Data
2. Secondary Data

29. 2-29
Distinction Between Primary Data and
Secondary Data
S.No. Description Primary Data Secondary Data
1. Source Original Source Secondary Source
2. Methods of Data
Collection
Observation method, Questionnaire
Method,etc.
Published data of government
agencies, Journals etc.
3. Originality of data Original : First time, Collected by user No : Data are collected by some
other agencies
4. Time More Less
5. Cost Expensive Cheaper
6. Efforts More Less
7. Accuracy More accurate Less accurate

30. 2-30
Primary Data
Collected by investigator himself for the purpose of a specific inquiry or study. The
data is original in character and highly representative and unbiased.
Methods of Primary Data Collection
 Observation
 Interviewing: a) Personal interviews b) Telephonic Interview
 Questionnaires

31. 2-31
Observation Method
Advantages:
Information is collected by observing the process at
work without asking from the respondent.
i) Subjective/response bias can be eliminated if
observation is done accurately.
ii) Information obtained relates to what is currently
happening, it is not complicated by either the past
behaviour or future attitudes.
iii) This method is independent of respondents’
willingness to respond .

32. 2-32
Limitations
Although this method can be used to study sales
techniques , customer movements, customer
response, etc, but this method has certain
limitations.
1. The information provided by this method is very
limited. Customer’s state of mind, their buying
motives, their income and education are not
revealed.
2. The method is time consuming.

33. 2-33
Interview Method
a) Personal Interviews : Personal Interview method
requires a person known as the interviewer asking
questions in a face-to-face contact to the other
person.
This sort of interview may be in the form of direct
personal investigation or indirect oral investigation.

34. 2-34
Merits of Personal Interviews Method
i) More information and that too in greater depth
can be obtained.
ii) The interviewer can simplify the language of the
questions ( if required) on the basis of ability
and education level of the interviewee and thus
misinterpretations or confusions regarding
questions can be avoided.

35. 2-35
Demerits of Personal Interviews Method
i) It is very expensive method, specially when large
and widely spread geographical sample is taken.
ii) This method is relatively more time-consuming
specially when the sample is large.
iii) Under the interview method, the organisation
has to select, train and supervise the field –staff
which is time consuming and difficult task.

36. 2-36
iv) Certain type of respondents such as important
officials or executives or people in high income
groups may not be easily approachable under
this method.
v) There remains the possibility of biasness of the
interviewer as well as of the respondents.

37. 2-37
b) Telephonic Interviews : In this method
information is collected by contacting
respondents on telephone itself.
It is not very widely used method, but plays
important part in industrial surveys, particularly
in developed regions.

38. 2-38
The Chief merits of such a system are:
i) It is a quick way of obtaining information.
ii) It is cheaper than personal interviewing
method; here the cost per response is relatively
low.
iii) Replies can be recorded without causing
embarrassment to respondents.
iv) No field staff is required.

39. 2-39
Contnd…
v) Wider sample can be covered.
vi) Certain type of respondents such as important
officials or executives or people in high income
groups who are not be easily approachable can
be contacted over telephone.

40. 2-40
Demerits
i) This method is restricted to those respondents
who have telephone facilities.
ii) Questions have to short and to the point.

41. 2-41
Questionnaire Technique
Gather data by asking questions from people who are thought to have the
desired information.
When information is to be collected by asking questions to people who
may have the desired data, a standardized form called questionnaire is
prepared.
The questionnaire has a list of questions to be asked in a desired
sequence and spaces in which the respondents record the answers.
Cont…
.

42. 2-42
Usefulness of Questionnaire Technique
This method of data collection is quite popular,
particularly in case of big enquires. It is being adopted
by private individuals, research workers, private and
public organisations .

43. 2-43
Questionnaire Technique
Advantages
 Yield broaden range of information compared with direct observation. One can
not know by observation why buyer makes particular purchases or what is his
opinion about a product.
 Fast
 Economical/low cost than observation method.
 By mailing or sending questionnaire by post the respondents who are not
approachable can also be reached conviently.
Cont…
.

44. 2-44
 Limitations
 Unwillingness of respondent to provide information. This requires
salesmanship on the part of the interviewer. The interviewer may assure that
the information will be kept secret. Motivating respondents with some token
gifts often yield result.
 Inability of respondents due to lack of knowledge.

45. 2-45
Essentials of a good questionnaire
1. To be successful, questionnaire should be comparatively
short and simple i.e. the size of the questionnaire should
be kept as minimum.
2. Questions should proceed in logical sequence from easy
to more difficult questions.
3. Personal and intimate questions should be left to the end.
4. Technical terms and vague expressions capable of
different interpretations should be avoided.

46. 2-46
5. Questions may be dichotomous (Yes or No answers),
multiple choice ( alternative answers listed) or open-
ended. The latter type of questions are often difficult to
analyse and hence should be avoided in a
questionnaire to the extent possible.
6. Questions affecting the sentiments of respondents
should be avoided.
7. Adequate space for answers should be provided in the
questionnaire to help editing and tabulation.
8. There should always be provision for indications of
uncertainty, e.g. ‘do not know’, “no preference” and so
on.

47. 2-47
9. Brief directions with regard to filling up the questionnaire
should invariably be given in the questionnaire itself.
10. Finally, the physical appearance of the questionnaire
affects the cooperation the researcher receives from the
recipients. Therefore, attractive looking questionnaire
particularly in mail surveys, is a plus point for getting
cooperation. The quality of the paper, along with its
colour , must be good so that it may attract the attention
of the recipients.

48. 2-48
Construction of a Questionnaire
 Decide what information is wanted
 Decide the type of questions
 Decide the content of individual questions
 Decide the wording of questions
 Decide the sequence of questions
 Decide the lay out and method of reproduction
 Make a preliminary draft and pretest it
 Revise and prepare the final draft

49. 2-49
Secondary Data
It is the data already been collected by others which may be published or
unpublished. This data is primary data for the agency that collects it and becomes
secondary data for someone else who uses this data for his own purpose.

50. 2-50
Methods of Secondary Data Collection
 Various publications of the central and state govt.
 Various publication of foreign governments/institutions
 Technical & trade journals
 Book, magazines and news-paper
 Reports and publications of universities/institutions
 Census reports
 Reports prepared by research scholars
 Researcher must ensure the reliability, suitability and adequacy of
secondary data