Population Collection of all individuals or objects or items under

study and denoted by N Sample A part of a population and denoted by n Variable Characteristic of an individual or object.

◦ Qualitative and Quantitative variables Parameter Characteristic of the population Statistic Characteristic of the sample

Sampling TechniquesSampling Techniques

S im p le R an d omS am p le

P rop ort ion a te D isp rop ort ion a te

S tra tified R an d omS am p le

S ys tem aticR an d omS am p le

O n e S tag e Tw o S tag e M u lt i S tag e

C lu s te rS am p lin g

P rob ab ilityS am p lin g

C on ven ien cesam p lin g

Q u otaS am p lin g

Ju d g em en tS am p lin g

S n owb a llS am p lin g

N on -P rob ab ilityS am p lin g

COMPARISON OF PROBABILITY SAMPLECOMPARISON OF PROBABILITY SAMPLEDescription Cost and Degree

of useAdvantages Disadvantages

Simple Random Sample:

Researcher assigns each member of the sampling frame a number, then selects sample units by a random method

High cost

Most likely used

Only minimal advance knowledge of population needed; easy to analyse data and compute error

Requires sampling frame to work from; Does not use knowledge of population; larger errors for same sample size than with stratified sampling.

Stratified Random sample:

Researcher divides the population into groups and randomly selects sub-samples from each group

High cost

Moderately used

Assures representation of all groups in sample;

Reduces variability for same sample size

Requires accurate information on proportion in each stratum; If stratified lists are not already available, they can be costly to prepare.

Systematic:

Researcher uses natural ordering or order of sampling frame, selects an arbitrary staring point, then selects items at a preselected intervals.

Moderate cost

Moderately used

Simple to draw sample; easy to check

If sampling interval is related to a periodic ordering of the population, may introduce increased variability.

Cluster sampling:

Researcher selects sampling units at random, then does complete observations of all units in the groups

Low cost

Frequently used

If clusters geographically defined, yields lowest field cost; requires listing of all clusters but of individuals only within clusters

Larger error for comparable size than other probability samples.

COMPARISON OF NON – PROBABILITY SAMPLECOMPARISON OF NON – PROBABILITY SAMPLEDescription Cost and Degree of

useAdvantages Disadvantages

Convenience:

Researcher uses most convenient sample or most economical sample

Very low cost

Extensively Used

No need to list of population

Variability and bias of estimates cannot be measured or controlled

Judgement:

An export or experienced researcher selects the sample to fulfill a purpose

Moderate cost

Average use

Useful for certain types of forecasting

Bias due to experts’ beliefs

Quota:

Researcher classifies population by pertinent properties, determines desired proportion of sample from each class

Moderate cost

Very extensively used

Introduces some stratification of population; requires no list of population

Bias in researcher’s classification of subjects.

Snowball:

Initial respondents are selected by probability samples; additional respondents are obtained by referral from initial respondents.

Low cost

Used in special situations

Useful in locating members of rare populations

High bias because sample units are not independent.

Types of measurement scales are

Nominal Scale Ordinal Scale Interval scale Ratio Scale

NominalOrdinalIntervalRatioPeople or objects with the same scale value are the same NominalOrdinalIntervalRatioPeople or objects with the same scale value are the same on some attribute. The values of the scale have no 'numeric' meaning in the way that on some attribute. The values of the scale have no 'numeric' meaning in the way that

you usually think about numbers.People or objects with a higher scale value have more you usually think about numbers.People or objects with a higher scale value have more of some attribute. The intervals between adjacent scale values are indeterminate. Scale of some attribute. The intervals between adjacent scale values are indeterminate. Scale

assignment is by the property of "greater than," "equal to," or "less than."Intervals assignment is by the property of "greater than," "equal to," or "less than."Intervals between adjacent scale values are equal with respect the the attribute being measured. between adjacent scale values are equal with respect the the attribute being measured.

E.g., the difference between 8 and 9 is the same as the difference between 76 and E.g., the difference between 8 and 9 is the same as the difference between 76 and 77.There is a rationale zero point for the scale. Ratios are equivalent, e.g., the ratio of 2 77.There is a rationale zero point for the scale. Ratios are equivalent, e.g., the ratio of 2

to 1 is the same as the ratio of 8 to 4.to 1 is the same as the ratio of 8 to 4.

Nominal Ordinal Interval Ratio

People or objects with the same scale value are the same on some attribute.

The values of the scale have no 'numeric' meaning in the way that you usually think about numbers.

People or objects with a higher scale value have more of some attribute.

The intervals between adjacent scale values are indeterminate.

Scale assignment is by the property of "greater than," "equal to," or "less than."

Intervals between adjacent scale values are equal with respect the attribute being measured.

E.g., the difference between 8 and 9 is the same as the difference between 76 and 77.

There is a rationale zero point for the scale.

Ratios are equivalent, e.g., the ratio of 2 to 1 is the same as the ratio of 8 to 4.

Nominal Ordinal Interval Ratio

Classification data:

e.g. Male / Female

No ordering:

e.g. it makes no sense to state that M > F

Arbitrary labels:

e.g., M/F, 0/1, etc

Ordered but differences between values are not important

e.g., Political parties on left to right spectrum given labels 0, 1, 2

e.g., Likert scales, rank on a scale of 1..5 your degree of satisfaction

e.g., Restaurant ratings

Ordered, constant scale, but no natural zero

Differences make sense, but ratios do not

e.g. Temperature (C,F), Dates

Ordered, constant scale, natural zero

e.g., Height,

Weight,

Age,

Length