BBA tigers of university of education lower mall campus lahore....... BBA5 (2012-16).

4. Group Members
 Junaid Hanif
 Asif Inam
 Farhan Yousaf
 Bilal Ikram
 Abdul Rehman
 Rida Tahir
 Adil Arif

5. Contents
 Sampling
 Probability Method
 Non Probability Method
 Sampling Size & Error

6. Sampling

7. What is Sampling?
 Sampling involves the selection of a number of study elements/units from
a defined study population.
 Sampling is the process of selecting elements from the study population in
such a way that the elements selected represent the population.

8. Concepts in Sampling
 A population (target population) is the entire collection of all the
elements that are of interest in a particular investigation.
 A single member of the population is referred to as a population element
 Sampled/Study Population is an aggregation of elements from which the
sample is actually drawn.
 A sample is a collection of elements (subset) drawn from the study
population.

9. Advantages of Sampling
 Reduces time and cost
 Saves labour
 Quality of Study is Better
 Provides quicker results
 Effective if population is infinite

10. Probability Sampling

11. What is Probability?
 Chances Of Occurrence
 Possible Outcomes Of Given Events Together

12. Probability Sampling Methods
 Random Sampling
 Stratified Sampling
 Systematic Sampling
 Cluster Sampling

13. Random Sampling
Each element in the population has an equal probability of selection AND each
combination of elements has an equal probability of selection.

14. Stratified Sampling
 Divide population into groups that differ in important ways
 Basis for grouping must be known before sampling
 Select random sample from within each group

15. Systematic Sampling
 Systematic sampling is a random sampling technique which is frequently
chosen by researchers for its simplicity and its periodic quality.
 Each element has an equal probability of selection, but combinations of
elements have different probabilities.
 Population size N, desired sample size n, sampling interval k=N/n.

16. Example
 A researcher wants to select a systematic random sample of 10 people from a
population of 100. If he or she has a list of all 100 people, he would assign
each person a number from 1 to 100. The researcher then picks a random
number, 6, as the starting number. He or she would then select every tenth
person for the sample (because the sampling interval = 100/10 = 10). The
final sample would contain those individuals who were assigned the following
numbers: 6, 16, 26, 36, 46, 56, 66, 76, 86, 96.

17. Examples of Systematic Samples
 A few examples of systematic samples follow below:
 Calling every 1000th person in the phone book to ask their opinion on a topic.
 Asking every university student with ID number ending in 11 to fill out a
survey.
 Stopping every 20th person on the way out of a restaurant to ask them to rate
their meal.

18. Advantages of Systematic Sampling
 The main advantage of using systematic sampling is its simplicity. It allows the
researcher to add a systematic element into the random selection of
subjects, yet it is very easy to do.
 Another advantage of systematic sampling is that the researcher is
guaranteed that the population will be evenly sampled. In simple random
sampling, there exists a chance that subjects are selected in clusters. This is
systematically eliminated in systematic sampling because the sample
elements are equal distances apart in the population.

19. Disadvantages of Systematic Sampling
 The biggest disadvantage of systematic sampling is that the process of
selecting the sample can interact with a hidden periodic trait within the
population. In an extreme example, let’s say every tenth person in the
population was Hispanic and the sampling technique coincided with the
periodicity of that trait. The selected sample would then be mostly (or all)
Hispanic, which would over represent Hispanics in the final sample. This
means the sampling technique is no longer random and the
representativeness of the sample is compromised.

20. Cluster Sampling
 In cluster sampling, instead of selecting all the subjects from the entire
population right off, the researcher takes several steps in gathering his
sample population.

21. Stratification vs. Clustering
Stratification
 Divide population into groups different from each other: sexes, races, ages
 Sample randomly from each group Less error compared to simple random
 More expensive to obtain stratification information before sampling
Clustering
 Divide population into comparable groups: schools, cities
 Randomly sample some of the groups
 More error compared to simple random
 Reduces costs to sample only some areas or organizations

22. Non Probability Sampling

23. Non-Probability Sampling
The process of selecting a sample from a population without using (statistical)
probability theory.

24. Types of Non-Probability Sampling
 Convenient (or Convenience) Sampling
 Judgment Sampling
 Quota Sampling
 Snowball Sampling

25. Convenience sampling
Convenience sampling is a non-probability sampling technique where subjects
are selected because of their convenient accessibility and proximity to the
researcher.

26. Examples
 For example, if a company wants to figure out what flavor of pizza sells the
best in college students, they could poll an average local college and reliably
say that that is an accurate representation of most college students. Their
research would not be accurate for the entire population, but the company
only wants to know what one group thinks. This method is most often used
in research when budgeting is an issue, or when it is not timely to use another
sampling technique.
 The administrators of a college have announced a sharp increase in tuition fees for
the next year.
 A TV reporter covering this news item is shown standing on campus talking to
several students, one at a time, about their reactions to the proposed tuition fee
increase.
 TV Reporter says: “While some of the students feel that the 10 percent fee hike is
justified, most of them consider it to be unfair.”

27. Judgmental/ purposive sampling
 The process whereby the researcher selects a sample based on experience or
knowledge of the group to be sampled.
 elements selected for the sample are chosen by the judgment of the
researcher.
 Researchers often believe that they can obtain a representative sample by
using a sound judgment, which will result in saving time and money”

28. Examples
 if the researcher are interested in the opinions of Pakistani females between 20 and 30
years old, they would stop the people passing by who look like they fit this description. One
of the first things the researcher will do in this situation is verify that the respondent does in
fact meet the characteristics or criteria for being included in the sample. If they do, the
researcher will ask them the rest of the survey questions. If they do not meet the criteria,
the researcher will likely send them on their way.
 For instance, if a researcher want to find out what factor lead to dengue disease the only
 people to be consulted for first hand information are the medical doctors who have expert
 Knowledge by virtue of their professional acumen to provide good data or information to
 The researcher .this technique is therefore useful when a limit number or category of people
 Have the information that is sought for by the researcher .

29. QOUTA SAMPLING
SNOWBALL SAMPLING

30. Quota Sampling
 Selecting participant in numbers proportionate to their numbers in the larger
population, no randomization.
 For example, the researcher might want to survey 100 males and 100 females. So, the
researcher continues to contact individuals until the sample has 100 males and 100
females.

31. Examples
1) For example, the researcher might want to survey 100 males and 100
females. So, the researcher continues to contact individuals until the
sample has 100 males and 100 females.
2) If u want to get a survey and need a sample for unemployed peoples
in Lahore .so you get exactly sample through survey that 60% young
peoples and 40% old peoples are unemployed .it is called quota
sampling .

32. Snowball sampling
Selecting a few individuals who can identify other
individuals who can identify still other individuals
who might be good participants for a study
• This procedure is appropriate for difficult to locate
populations or persons with specific characteristics:
• Vietnam veterans who fought in a specific area of
the country.
• Influential leaders in a community.
• Persons who wish to remain anonymous, but who
will respond to introductions from their associates.

33. Example
 For instance, if someone was attempting to do a research sample involving
football players because they were trying to sell a customized piece of
equipment, they would need to meet with some players to get their point of
view about the product. If the researcher only knew a few players, they
would have to go out and personally introduce themselves to other players to
expand their study. They could contact the player or players that they already
know and ask them to refer them to a few others. They could offer a small
incentive to quicken the process, and maybe this perk would attract other
players to participate in the study. They could also gain access to the roster
from the school’s website and try and contact players via email or telephone.
The more relationships they create, the more information they will receive. If
they put the effort in to meet with a few kids from a few different teams,
they would have the opportunity to be referred to by every kid on the team.
The snowball effect would occur as more and more referrals are acquired.

34. Sampling Size

35. WHAT IS SAMPLE SIZE?
 This is the sub-population to be studied in order to make an inference to a reference
population(A broader population to which the findings from a study are to be
generalized)
 In census, the sample size is equal to the population size. However, in research,
because of time constraint and budget, a representative sample are normally used.
 The larger the sample size the more accurate the findings from a study.

36.  Availability of resources sets the upper limit of the sample size.
 While the required accuracy sets the lower limit of sample size
 Therefore, an optimum sample size is an essential component of any research.

38. WHAT IS SAMPLE SIZE DETERMINATION?
Sample size determination is the mathematical estimation of the number of
subjects/units to be included in a study.
When a representative sample is taken from a population, the finding are generalized
to the population.
Optimum sample size determination is required for the following reasons:
1. To allow for appropriate analysis
2. To provide the desired level of accuracy
3. To allow validity of significance test.

39. HOW LARGE A SAMPLE DO I NEED?
If the sample is too small:
1. Even a well conducted study may fail to answer it research question
2. It may fail to detect important effect or associations
3. It may associate this effect or association imprecisely

40. CONVERSELY
If the sample size is too large:
1. The study will be difficult and costly
2. Time constraint
3. Available cases e.g. rare disease.
4. Loss of accuracy.
Hence, optimum sample size must be determined before commencement of a study.

41.  Given two exactly the same studies, methods & population, the study with a larger
sample size will have less sampling process error compared to the study with smaller
sample size. Keep in mind that as the sample size increases, it approaches the size of
the entire population also increased.

42. Sampling Error

43. SAMPLING ERROR
 A statistical error to which an analyst exposes a model simply because he or
she is working with sample data rather than population or census data. Using
sample data presents the risk that results found in an analysis do not
represent the results that would be obtained from using data involving the
entire population from which the sample was derived.

44. NON SAMPLING ERROR
 Non-sampling errors may stem from many sources in the various stages of
collecting and processing the survey data and may occur equally in a full
census.

45. The Main non-sampling errors
A. Errors stemming from non-response:

 errors caused by the fact that households are not investigated due to
absence from home or refusal to participate. This may cause some bias in the
estimates, since the characteristics of persons belonging to these households
may differ from those of persons who were investigated.

46.  B) Errors stemming from non-response:
 Errors caused by the fact that households are not investigated due to
absence from home or refusal to participate. This may cause some bias in the
estimates, since the characteristics of persons belonging to these households
may differ from those of persons who were investigated.
 C) Errors in processing:
 Errors that occur at the stage of processing the material, such as errors in
coding and in the data entry process of the questionnaires. Some of these
errors are corrected by means of checks that the material undergoes

47.  D) Some of the households were interviewed in a week which was not the
“determinant week” This also causes a bias in the estimates.
 In contrast to sampling errors, which can be estimated on the basis of the
survey data, no sampling errors are difficult or even impossible to estimate.
Thus, emphasis is laid on controlling such errors, rather than on indicating
their magnitude in the data.