Note 3 Sampling,Frequency Distribution & Data Presentation Dr.Nora 070313

APPLIED QUANTITATIVE METHODS (RPG 131)

Semester 2, Academic Session 2012/2013

Topic: Sampling, Frequency Distribution & Data Presentation

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Dr.Norazmawati Md.Sani @ Abd.Rahim (Dr.Nora)E08/102B

[email protected]

http://drnorazmawati.blogspot.com7 March 2013

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Content

1. Sampling2. Sampling Design3. How To Use Data

4. What Is Statistics?5. Why Study Statistics?6. Why We Need Statistical Test?7. Types of Variables8. Basic Quantitative Research Approach9. Conclusions

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Population

Population Frame

ElementSubject

Sample

Sampling

Sampling

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Population

Def; Refers to the entire group of people, events/things of interest that the researcher

want to investigate.

Example: If the researcher is interested in investigating the student’s performance in USM, then all the USM students will form

the population.

We leadPopulation Frame

Def; Is a listing of all the elements in the population from which the sample is to be

drawn.

Example: Population refers to all USM students.

1. Ali Bin Ahmad2. Adam Bin Mansor

3. Anisah Bt Alias4. Atiqah Bt Mustafa

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Def; An element is a single member of the population.

Example: Each USM students is an element.

Element

1st Year

2nd Year3rd Year

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1st Year

2nd Year3rd Year

Subject

Def; Is a single member of the sample.

Example: 100 members from the total population formed the sample for the study; each USM students in the sample is a subject.

100

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2nd Year3rd Year

Sample

Def; Is a subset/subgroup of the population. It comprises some members selected from the population. SOME but not ALL elements of the population would form a sample.

Example: 1st year students in USM.

1st Year

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Def; Is the process of selecting a sufficient number of elements from

the population. Example;

100 500 1000

Sampling

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Sampling?

Practically impossible to collect data/to

test/to examine every element.

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Limitation in time, cost & other human resources.

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Why Sampling?

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Sampling Design

Probability Sampling

Non Probability Sampling

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Unrestricted/Simple Random Sampling

The elements in the population have some known chance/probability of being selected as sample subject.

Example; there are 1000 elements in the population & we need a sample of the probability of any one of them being chosen as a subject is 0.1

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Restricted/Complex Probability Sampling

Offer a viable & sometimes more

efficient alternative to the cumbersome

of unrestricted sampling.

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Systematic Sampling

• Involves drawing every ‘n’th element in the population starting with a randomly

chosen element.

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Systematic Sampling

• Example; We want a sample of 35 households from a total population of 260 houses in a particular locality, then we could sample every seventh house starting from a random number from 1 to 7. Let us say that the random number is 7, then houses numbered 7,14, 21, 28, 35 & so on.

714

21 28

35

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Stratified Random Sampling

• Involves a process of stratification/segregation. Use when there may be identifiable subgroups of elements within the population that may be expected to have different parameters on a

variable of interest to the researchers.

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Stratified Random Sampling

• Example; Managing Director interested in assessing the motivational level of their employees. This will be different for different group of people such as managerial level, supervisory level &

clerical level. The result can be used for Managing Director to focus on certain

group that has low motivation.

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• In the stratified random sampling there is homogeneity within

group & heterogeneity across groups.

CS

Stratified Random Sampling

M

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CSM

Cluster Sampling

Offer more heterogeneity within groups & more homogeneity among groups.

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Cluster Sampling

A good example of different cluster are inputs offered by various department of

company president to enable him to make a decision on product

development, budget allocations & marketing strategies. Product

DevelopmentBudget

AllocationsMarketing Strategies

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Area Sampling

• Is a form of cluster sampling within an area. Use

when the research pertains to populations within identifiable geographical areas such as countries, city blocks/particular boundaries within a locality.

• Example; sampling the needs of consumers before opening a 24 hour convenience store in a

particular part of town.

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Double Sampling• When a sample is used in a study to collect some preliminary information of interest, & later a subsample of this primary sample is used to examine the matter in more detail.

• Example; a structured interview might indicate that a subgroup of the respondents has more insight into the problems of the organization. These respondents might be interviewed again withadditional questions.

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Non Probability SamplingThe elements do not have a known/

predetermined chance of being selected as subject.

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Non probability Sampling

• It means that the findings from the

study of the sample cannot be confidently

generalized to the population.

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• However researchers more concern about obtaining some

preliminary information in a quick & inexpensive way.

Non probability Sampling

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Non Probability Sampling

Convenience sampling Purposive sampling

Judgment sampling

Quota sampling

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Convenience Sampling

• Involves collecting information from members of the population who are conveniently available to provide this information.

• Example; ‘Pepsi Challenge’ contest with the purpose of determining whether people prefer one product over another, might be set up at a shopping mall visited by many shoppers.

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Purposive Sampling

• Sometimes it necessary to obtain information

from specific targets, who will be able to provide

the desired information/because they fulfill the criteria set up by researcher.

2nd Year

Have 2 major type such as; Judgment sampling Quota sampling.

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Judgment Sampling• Involves the choice of subjects who are in the best

position to provide the information required.

• Example; if a researcher wants to find out what it takes for women managers to make it to the top, the only people can give first hand information are the women managers who are the presidents, vice-presidents & important top level executives in work organization. This is because they have a knowledge & perhaps be able to provide good data/information to the researcher.

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Quota Sampling

• Is a form of proportionate stratified sampling in which a predetermined proportion of people are sampled from different groups, but on a convenience basis.

• Example; The work attitude of Blue-collar Workers (BCW) in an organization are quite different from those of White-collar Workers (WCW). If there are 60% of BCW & 40% of WCW, & if total 30 people are to be interviewed, then a quota of 18 BCW & 12 WCW will be form of sample.

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How To Use Data Data collection must relate to the research

questions. Eg. Student’s profile, absenteeism.

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The science of collecting, organizing, presenting, analyzing, & interpreting data to assist in

making more effective decisions.

What Is Statistics?

1

2

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Why Study Statistics?

Numerical information is anywhere (in the newspapers, magazines reported the

numerical information).

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Statistical techniques are used to make decisions that affect our daily lives.

Example: Medical researcher study the cure rates for certain diseases, based on the use

of different drugs & different treatments.

Why Study Statistics?

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The knowledge of statistical methods will help you understand

why decisions are made & give you a better understanding of

how they affect you. This is related with conducting research.

Why Study Statistics?

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Because in research, we seek scientific facts & answers to the research questions we

have, by analyzing the data.

Why We Need Statistical Test?

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A variables must have different values for example

gender, age, ethnic group etc.

Why We Need Statistical Test?

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Organizing the data: analyzing them, & making sense of the results, we can answer our research

questions.

Why We Need Statistical Test?

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Data refers to the available raw information gathered.

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Types of Variables

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Qualitative

Def; when the variable being studied is nonnumeric.

– Example: State of birth, eye color.

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Def; When the variable studied can be reported numerically.

Quantitative

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Types of Quantitative Variables

Quantitative variables

Discrete variablesHas certain values. Eg: No. of children, no. of employees.

Continuous variablesCan assume any valuewithin specific range.

Eg: distance from KL to Penang.

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Basic Comparative

Approach

Basic Associational

Approach

Basic Descriptive

Approach

Basic Quantitative Research Approach

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• Comparison between groups. The IV had only 2 values/categories so a statistical comparison between the groups would be perform. – For example the study to compared 2 groups of student

on exam performance scores.

Men

Women

Exam Performance Scores

IV DV

Basic Comparative Approach

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Men

Women

Exam Performance Scores

IV DV

The result could be the exam performance scores of the women will be significantly

higher than the exam performance scores for men.

Basic Comparative Approach

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• Use where the IV is usually continuous/has several ordered categories, usually 5/>. – For example age & self confidence.

Age20-2930-3940-4950-5960-69

Self confidence

Basic Associational Approach

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• Has only 1 variable at a time so that no relationships are made.

Basic Descriptive Approach

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• Most research studies include some descriptive questions to describe the

sample.

Basic Descriptive Approach

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CONCLUSIONS

6 elements of sampling. Sampling Design =Probability & Non Probability

sampling. How To Use Data = Data collection must relate to

the research questions. Statistics = Science of collecting, organizing,

presenting, analyzing, & interpreting data to assist in making more effective decisions.

2 Types of Variables = Discrete & Continuous Variables.

3 Basic Quantitative Research Approach.

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References

• Research methods for business: A Skill Building Approach by Uma Sekaran. John wiley and Sons,Inc.1992.

• Basic Statistics for business and Economics: Third Edition by Douglas A. Lind, Robert D. Mason and William G. Marchal, Mc Graw Hill, 1994

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