Jun 11, 2015
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