Group Members

Junaid Hanif

Asif Inam

Farhan Yousaf

Bilal Ikram

Abdul Rehman

Rida Tahir

Adil Arif

Contents

Sampling

Probability Method

Non Probability Method

Sampling Size & Error

Sampling

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.

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 studypopulation.

Advantages of Sampling

Reduces time and cost

Saves labour

Quality of Study is Better

Provides quicker results

Effective if population is infinite

Probability Sampling

What is Probability?

Chances Of Occurrence

Possible Outcomes Of Given Events Together

Probability Sampling Methods

Random Sampling

Stratified Sampling

Systematic Sampling

Cluster Sampling

Random Sampling

Each element in the population has an equal probability of selection AND each

combination of elements has an equal probability of selection.

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

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.

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.

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.

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.

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.

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.

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

Non Probability Sampling

Non-Probability Sampling

The process of selecting a sample from a population without using (statistical)

probability theory.

Types of Non-Probability Sampling

Convenient (or Convenience) Sampling

Judgment Sampling

Quota Sampling

Snowball Sampling

Convenience sampling

Convenience sampling is a non-probability sampling technique where subjects

are selected because of their convenient accessibility and proximity to the

researcher.

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.”

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”

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 .

QOUTA SAMPLING

SNOWBALL SAMPLING

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.

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 .

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.

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.

Sampling Size

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.

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.

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.

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

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.

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.

Sampling Error

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.

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.

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.

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

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.