PRESENTED BY :Ashwani SharmaAlankar MathurSandeep sharma

Lalit Gautam

Introduction: Population and Sample Benefits And Limitation Sample Process Chart Types Of Sample Process Probablity Types Non-Probablity types

Population – group of things (people) having one or more common characteristics

Sample – representative subgroup of the larger population◦Used to estimate something about a population

(generalize)◦Must be similar to population on characteristic being

investigatedA sample is a “part of a whole to show what the rest is

like”.Sampling helps to determine the corresponding value of

the population and plays a vital role in marketing research

Probability Sampling Simple random sampling Stratified random sampling Systematic sampling Cluster (area) sampling Multistage sampling

Non-Probability Sampling Deliberate (quota) sampling Convenience sampling Purposive sampling

Samples offer many benefits: Save costs: Less expensive to study the sample than the

population. Save time: Less time needed to study the sample than the

population . Accuracy: Since sampling is done with care and studies are

conducted by skilled and qualified interviewers, the results are expected to be accurate.

Destructive nature of elements: For some elements, sampling is the way to test, since tests destroy the element itself

Limitations of Sampling Demands more rigid control in undertaking sample

operation. Minority and smallness in number of sub-groups often

render study to be suspected. Accuracy level may be affected when data is subjected

to weighing. Sample results are good approximations at best

Defining the

population

Developing a sampling

Frame

DeterminingSample

Size

SpecifyingSample Method

SELECTING THE SAMPLE

Universe or population is the whole mass under study. How to define a universe:

◦ What constitutes the units of analysis (HDB apartments)?◦ What are the sampling units (HDB apartments occupied in

the last three months)?◦ What is the specific designation of the units to be covered

(HDB in town area)?◦ What time period does the data refer to (December 31, 1995)

A sample frame is the list of all elements in the population (such as telephone directories, electoral registers, club membership etc.) from which the samples are drawn.

A sample frame which does not fully represent an intended

population will result in frame error and affect the degree of reliability of sample result

Sample size may be determined by using:◦ Subjective methods (less sophisticated methods)

The rule of thumb approach: eg. 5% of population Conventional approach: eg. Average of sample sizes of

similar other studies; Cost basis approach: The number that can be studied

with the available funds;◦ Statistical formulae (more sophisticated methods)

Confidence interval approach.

Sample sizes used in different marketing research studies

TYPE OF STUDY MINIMUM

SIZE

TYPICAL

RANGE

Identifying a problem (e.g.marketsegmentation) 500 1000-2500Problem-solving (e.g., promotion) 200 300-500

Product tests 200 300-500

Advertising (TV, Radio, or print Mediaper commercial or ad tested) 150 200-300Test marketing 200 300-500Test market audits 10

stores/outlets10-20

stores/outletsFocus groups 2 groups 4-12 groups

To determine sample sizes using statistical formulae, researchers use the confidence interval approach based on the following factors: ◦ Desired level of data precision or accuracy;◦ Amount of variability in the population (homogeneity);◦ Level of confidence required in the estimates of population values.

Availability of resources such as money, manpower and time may prompt the researcher to modify the computed sample size.

Probability Sampling◦ Every element in the target population or universe [sampling

frame] has equal probability of being chosen in the sample for the survey being conducted.

◦ Scientific, operationally convenient and simple in theory.

◦ Results may be generalized. Non-Probability Sampling

◦ Every element in the universe [sampling frame] does not have equal probability of being chosen in the sample.

◦ Operationally convenient and simple in theory.

◦ Results may not be generalized.

Probability sampling:Four types of probability sampling

Appropriate for homogeneous population◦ Simple random sampling

Requires the use of a random number table.

◦ Systematic sampling Requires the sample frame only, No random number table is

necessary

Appropriate for heterogeneous population◦ Stratified sampling

Use of random number table may be necessary

◦ Cluster sampling Use of random number table

may be necessary

Four types of non-probability sampling techniques ◦ Very simple types, based on subjective criteria Convenient sampling Judgmental sampling

◦ More systematic and formal Quota sampling

◦ Special type Snowball Sampling

Equal probability Techniques

◦ Fishbowl (with replacement & w/o replacement)◦ Table of random numbers

Advantage ◦ Most representative group

Disadvantage ◦ Difficult to identify every member of a population

Random number tables consist of a randomly generated series of digits (0-9).  To make them easy to read there is typically a space between every 4th digit and between every 10th row.  When reading from random number tables you can begin anywhere (choose a number at random) but having once started you should continue to read across the line or down a column and NOT jump about.

Here is an extract from a table of random sampling numbers:

3680    2231    8846    5418    0498    5245    7071    2597 If we were doing market research and wanted to sample two houses from a street

containing houses numbered 1 to 48 we would read off the digits in pairs36    80    22    31    88    46    54    18    04    98    52    45    70    71    25    97 and take the first two pairs that were less than 48, which gives house numbers 36 and 22.

If we wanted to sample two houses from a much longer road with 140 houses in it we would need to read the digits off in groups of three:368    022    318    846    541    804    985    245    707    1 25    97and the numbers underlined would be the ones to visit: 22 and 125.

Houses in a road usually have numbers attached, which is convenient (except where there is no number 13). In many cases, however, one has first to give each member of the population a number. For a group of 10 people we could number them as:

0Appleyard,5Francis,1Banyard,6Gray,2Croft,7Hibbert,3Durran,8Jones,4Entwhistle,9Lillywhite

By numbering them from 0 to 9 you need only use single digits from the random number table.   36802231884654180498524570712597

Also called random sampling Simplest method of

probability sampling

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 37 75 10 49 98 66 03 86 34 80 98 44 22 22 45 83 53 86 23 51

2 50 91 56 41 52 82 98 11 57 96 27 10 27 16 35 34 47 01 36 08

3 99 14 23 50 21 01 03 25 79 07 80 54 55 41 12 15 15 03 68 56

4 70 72 01 00 33 25 19 16 23 58 03 78 47 43 77 88 15 02 55 67

5 18 46 06 49 47 32 58 08 75 29 63 66 89 09 22 35 97 74 30 80

6 65 76 34 11 33 60 95 03 53 72 06 78 28 14 51 78 76 45 26 45

7 83 76 95 25 70 60 13 32 52 11 87 38 49 01 82 84 99 02 64 00

8 58 90 07 84 20 98 57 93 36 65 10 71 83 93 42 46 34 61 44 01

9 54 74 67 11 15 78 21 96 43 14 11 22 74 17 02 54 51 78 76 76

10 56 81 92 73 40 07 20 05 26 63 57 86 48 51 59 15 46 09 75 64

11 34 99 06 21 22 38 22 32 85 26 37 00 62 27 74 46 02 61 59 81

12 02 26 92 27 95 87 59 38 18 30 95 38 36 78 23 20 19 65 48 50

13 43 04 25 36 00 45 73 80 02 61 31 10 06 72 39 02 00 47 06 98

14 92 56 51 22 11 06 86 88 77 86 59 57 66 13 82 33 97 21 31 61

15 67 42 43 26 20 60 84 18 68 48 85 00 00 48 35 48 57 63 38 84

Need to useNeed to useRandom Number Table

Systematic Sampling

•Technique •Use “system” to select sample (e.g., every 5th item in alphabetized list, every 10th name in phone book)

•Advantage •Quick, efficient, saves time and energy

•Disadvantage •Not entirely bias free; each item does not have equal chance to be selected•System for selecting subjects may introduce systematic error•Cannot generalize beyond pop actually sampled

Very similar to simple random sampling with one exception. In systematic sampling only one random number is needed throughout the

entire sampling process. To use systematic sampling, a researcher needs:

[i] a sampling frame of the population; and is needed.[ii] a skip interval calculated as follows:

Skip interval = population list size Sample size

Names are selected using the skip interval. If a researcher were to select a sample of 1000 people using the local telephone

directory containing 215,000 listings as the sampling frame, skip interval is[215,000/1000], or 215. The researcher can select every 215th name of the entiredirectory [sampling frame], and select his sample.

Example: How to Take a Systematic Sample

Step 1: Select a listing of the population, say the City Telephone Directory, from which tosample. Remember that the list will have an acceptable level of sample frame error.

Step 2: Compute the skip interval by dividing the number of entries in the directory by thedesired sample size.Example: 250,000 names in the phone book, desired a sample size of 2500,

So skip interval = every 100th name

Step 3: Using random number(s), determine a starting position for sampling the list.

Example: Select: Random number for page number. (page 01) Select: Random number of column on that page. (col. 03)

Select: Random number for name position in that column (#38, say, A..Mahadeva) Step 4: Apply the skip interval to determine which names on the list will be in the sample.

Example: A. Mahadeva (Skip 100 names), new name chosen is A Rahman b Ahmad.

Step 5: Consider the list as “circular”; that is, the first name on the list is now the initial nameyou selected, and the last name is now the name just prior to the initially selected one.Example: When you come to the end of the phone book names (Zs), just continue on

through the beginning (As).

Stratified Sampling

•Technique•Divide population into various strata•Randomly sample within each strata•Sample from each strata should be proportional

•Advantage •Better in achieving representativeness on control variable

•Disadvantage •Difficult to pick appropriate strata•Difficult to ID every member in population

Stratified sampling

A three-stage process:

Step 1- Divide the population into homogeneous, mutually exclusive and collectively exhaustive subgroups or strata using some stratification variable;

Step 2- Select an independent simple random sample from each stratum.

Step 3- Form the final sample by consolidating all sample elements chosen in step 2.

May yield smaller standard errors of estimators than does the simple random sampling. Thus precision can be gained with smaller sample sizes.

Stratified samples can be:

Proportionate: involving the selection of sample elements from each stratum, such that the ratio of sample elements from each stratum to the sample size equals that of the population elements within each stratum to the total number of population elements.

Disproportionate: the sample is disproportionate when the above mentioned ratio is unequal.

Example:

In general the size of the sample in each stratum is taken in proportion to the size of the stratum.  This is called proportional allocation.  Suppose that in a company there are the following staff:

•Male Full time:90•Male ,Part time:18•Female,Full time:09•Female,Part time:63

And we are asked to take a sample of 40 staff, stratified according to the above categories.The first step is to find the total number of staff (180) and calculate the percentage in each group.% male, full time = ( 90 / 180 ) x 100 = 0.5 x 100 = 50% male, part time = ( 18 / 180 ) x100 = 0.1 x 100 = 10% female, full time = (9 / 180 ) x 100 = 0.05 x 100 = 5% female, part time = (63/180)x100 = 0.35 x 100 = 35This tells us that of our sample of 40,

50% should be male, full time.10% should be male, part time.5% should be female, full time.35% should be female, part time.

50% of 40 is 20.10% of 40 is 4.5% of 40 is 2.35% of 40 is 14.

Cluster (Area) Sampling

•Randomly select groups (cluster) – all members of groups are subjects•Appropriate when

•you can’t obtain a list of the members of the population •have little knowledge of pop characteristics•Pop is scattered over large geographic area

•Advantage More practical, less costlyConclusions should be stated in terms of cluster (sample unit – school)Sample size is # of clusters

Is a type of sampling in which clusters or groups of elements are sampled at the same time. Such a procedure is economic, and it retains the characteristics of probability sampling.A two-step-process:

Step 1- Defined population is divided into number of mutually exclusive and collectively exhaustive subgroups or clusters;Step 2- Select an independent simple random sample of clusters.

One special type of cluster sampling is called area sampling, where pieces of geographical areas are selected.

Clusters are parts of the population with almost same elements or units of the population but in smaller scale. Cluster sampling can be done in just one step or multiple steps. In one step cluster sampling after selecting few clusters, we include whole selected cluster’s unit in to our sample while in multi stage cluster sampling we will choose randomly just some units within clusters.

Example: In assessing the satisfaction of HIV positive patients from hospital based health care services in city of Kerman you can assume each hospital in the city and allocate a random sample size from each hospital to reach your desired sample size. Tehran has around 120 hospitals; in one step method all patients form each hospital should be included while in multistage cluster sampling we first select some hospitals then a number of patients within each hospital (clusterwill be selected.

Stratified Sampling vs Cluster Sampling

Stratified Sampling Cluster Sampling1. The target population is sub-divided

into a few subgroups or strata, eachcontaining a large number of elements.

1. The target population is sub-divided into a large number ofsub-population or clusters, eachcontaining a few elements.

2. Within each stratum, the elements arehomogeneous. However, high degree ofheterogeneity exists between strata.

2. Within each cluster, the elementsare heterogeneous. Betweenclusters, there is a high degree ofhomogeneity.

3. A sample element is selected each time. 3. A cluster is selected each time.4. Less sampling error. 4. More prone to sampling error.5. Objective is to increase precision. 5. Objective is to increase sampling

efficiency by decreasing cost.

AREA SAMPLING

A common form of cluster sampling where clusters consist of geographic areas, such asdistricts, housing blocks or townships. Area sampling could be one-stage, two-stage, ormulti-stage.

How to Take an Area Sample Using SubdivisionsYour company wants to conduct a survey on the expected patronage of its new outlet in a new

housing estate. The company wants to use area sampling to select the sample households to beinterviewed. The sample may be drawn in the manner outlined below.___________________________________________________________________________________Step 1: Determine the geographic area to be surveyed, and identify its subdivisions. Each

subdivision cluster should be highly similar to all others. For example, choose ten housingblocks within 2 kilometers of the proposed site [say, Model Town ] for your new retail outlet;assign each a number.

Step 2: Decide on the use of one-step or two-step cluster sampling. Assume that you decide to use a two-stage cluster sampling.Step 3: Using random numbers, select the housing blocks to be sampled. Here, you select 4 blocks randomly, say numbers #102, #104, #106, and #108.

Step 4: Using some probability method of sample selection, select the households in each of thechosen housing block to be included in the sample. Identify a random starting point (say,apartment no. 103), instruct field workers to drop off the survey at every fifth house(systematic sampling).

Convenience sampling◦ Drawn at the convenience of the researcher. Common in exploratory research.

Does not lead to any conclusion. Judgmental sampling

◦ Sampling based on some judgment, gut-feelings or experience of the researcher. Common in commercial marketing research projects. If inference drawing is not

necessary, these samples are quite useful. Quota sampling

◦ An extension of judgmental sampling. It is something like a two-stage judgmental sampling. Quite difficult to draw.

Snowball sampling◦ Used in studies involving respondents who are rare to find. To start with, the

researcher compiles a short list of sample units from various sources. Each of these respondents are contacted to provide names of other probable respondents.

“Take them where you find them” - nonrandom Intact classes, volunteers, survey respondents (low return),

a typical group, a typical person Disadvantage: Selection bias Use post hoc analysis to show groups were equal at the

start

One of the most common examples of convenience sampling is using student volunteers as subjects for the research. Another example is using subjects that are selected from a clinic, a class or an institution that is easily accessible to the researcher. A more concrete example is choosing five people from a class or choosing the first five names from the list of patients.

In these examples, the researcher inadvertently excludes a great proportion of the population. A convenience sample is either a collection of subjects that are accessible or a self selection of individuals willing to participate which is exemplified by your volunteers.

This type of sampling technique is also known as purposive sampling and authoritative sampling.

Purposive sampling is used in cases where the specialty of an authority can select a more representative sample that can bring more accurate results than by using other probability sampling techniques. The process involves nothing but purposely handpicking individuals from the population based on the authority’s or the researcher’s knowledge and judgment.

In a study wherein a researcher wants to know what it takes to graduate summa cum laude in college, the only people who can give the researcher first hand advise are the individuals who graduated summa cum laude. With this very specific and very limited pool of individuals that can be considered as a subject, the researcher must use judgmental sampling.Judgmental sampling design is usually used when a limited number of individuals possess the trait of interest.

Similar to stratified random sampling Technique

◦ Quotas set using some characteristic of the population thought to be relevant

◦ Subjects selected non-randomly to meet quotas (usu. convenience sampling)

Disadvantage◦ selection bias◦ Cannot set quotas for all characteristics important to

study

The first step in non-probability quota sampling is to divide the population into exclusive subgroups.

Then, the researcher must identify the proportions of these subgroups in the population; this same proportion will be applied in the sampling process.

Finally, the researcher selects subjects from the various subgroups while taking into consideration the proportions noted in the previous step.

The final step ensures that the sample is representative of the entire population. It also allows the researcher to study traits and characteristics that are noted for each subgroup.

Quota Sampling To select a quota sample comprising 3000 persons in country X using three control

characteristics: sex, age and level of education. Here, the three control characteristics are considered independently of one another.

In order to calculate the desired number of sample elements possessing the various

attributes of the specified control characteristics, the distribution pattern of thegeneral population in country X in terms of each control characteristics is examined.

ControlCharacteristics Population Distribution Sample Elements .

Gender: .... Male...................... 50.7% Male 3000 x 50.7% = 1521................. Female .................. 49.3% Female 3000 x 49.3% = 1479

Age: ......... 20-29 years ........... 13.4% 20-29 years 3000 x 13.4% = 402................. 30-39 years ........... 53.3% 30-39 years 3000 x 52.3% = 1569................. 40 years & over .... 33.3% 40 years & over 3000 x 34.3% = 1029

Religion: .. Christianity ........... 76.4% Christianity 3000 x 76.4% = 2292................. Islam ..................... 14.8% Islam 3000 x 14.8% = 444................. Hinduism .............. 6.6% Hinduism 3000 x 6.6% = 198................. Others ................... 2.2% Others 3000 x 2.2% = 66

__________________________________________________________________________________

Purposive Sampling:

•Purposive sampling (criterion-based sampling)Establish criteria necessary for being included in study and find sample to meet criteria

•Solution: ScreeningUse random sampling to obtain a representative sample of larger population and then those subjects that are not members of the desired population are screened or filtered out

•EX: want to study smokers but can’t identify all smokers

Probability Evaluation Criteria Non-probability sampling sampling Conclusive Nature of research Exploratory

Larger sampling Relative magnitude Larger non-sampling

errors sampling vs. error non-sampling error

High Population variability Low[Heterogeneous] [Homogeneous]

Favorable Statistical Considerations Unfavorable

High Sophistication Needed Low

Relatively Longer Time Relatively shorter

High Budget Needed Low

• Selection process (random or not) – most important for generalizability

• Types of variables studied• how data are collected• Statistical procedures• Public relations Experimental studies use less subjects than

descriptive because attrition usually less; nonresponse in questionnaire studies important

Are subjects with special characteristics necessary for your research? (age, gender, trained/untrained, expert/novice, size, etc.)

Can you obtain the necessary permission and cooperation from the subjects?

Can you find enough subjects?

Interaction among selection of subjects, treatments, and measures is essential for experimental studies.

State how many subjects were selected Describe how the subjects were selected Discuss whether any subjects were lost during the

study and why Explain why the subjects were selected Describe subject characteristics that are pertinent to

study – be very specific Identify procedures taken to protect the subjects

David J. Luck and Ronald S. Rubin (Marketing research 7th edition) Harper W. Boyd, Jr Ralph ,Westfal ,tanley F.

tasch (Marketing research 7th edition) Cochrane W.G. Sampling Techniques. 3rd ed. N.Y. John

Willey & Sons. 1983 Overview Of Sampling Procedure,Fairfax County

Department Of Human Resources Management System. The World OF “Google”….

THANK YOU!

Presented By:Lalit GautamSandeep SharmaAlamkar MathurAshwani Kumar