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DATA AND DATA COLLECTION continued

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Title: DATA AND DATA COLLECTION continued


1
DATA AND DATA COLLECTION- continued
  • Lecture 4

2
Sample Selection
  • Population
  • Sample frame
  • Sample size
  • Sampling error

3
Sample Selection
  • Population
  • The total collection of elements about which
    inferences are to be made
  • The element is the subject on which measurement
    will be made

4
Sample Selection
  • Sample
  • Some elements drawn from the population
  • E.g. 10 students selected out of the MBA class
  • Sample frame
  • A listing of all elements in a population
  • E.g. List of all MBA Finance students

5
Sample Selection
  • Sample size
  • The number of the elements selected for use
  • Methods of determining size will be addressed
    later
  • Sampling Error

6
Sample Selection
  • Purpose of sampling
  • Population too large to study not feasible to
    study the whole or lower cost
  • Information drawn from sample may be better than
    from the whole population (Greater accuracy)
  • Better counts and observations because of small
    numbers
  • Greater speed of data collection
  • Availability of population elements

7
Sample Selection
  • The important thing about samples is how
    REPRESENTATIVE it is of the population

8
Principles of Sampling
  • Probability
  • Non-Probability

9
Probability Sampling
  • Simple random sampling
  • Systematic Sampling
  • Stratified sampling
  • Cluster/Area sampling
  • Multi-stage sampling
  • Multi-phase sampling

10
Probability Sampling
  • Simple random sampling
  • Write names on paper
  • Fold
  • Shuffle
  • Blindfold
  • Pick numbers wanted
  • Use of random numbers tables

11
Probability Sampling
  • Systematic Sampling
  • Decide on sample size
  • Express in percent
  • Choose the 1st element
  • e.g. 4 of population as sample
  • Select 1st 25 population member (random sampling)
  • then later every 25th member (not random)
  • Subsequent once are in fixed intervals

12
Probability Sampling
  • Pluses of systematic sampling
  • Spread over the entire sample
  • Easier and less costly
  • Can be used even with large populations
  • Minuses
  • Inefficient if there is hidden periodicity in the
    population
  • Useful when
  • Lists are available
  • Of considerable length

13
Probability Sampling
  • Stratified sampling
  • Population is subdivided into to subpopulations
    or strata
  • these are expected to be more homogeneous than
    the whole population
  • Random samples or systematic sampling is applied
    to each stratum
  • Useful for
  • Non homogeneous population

14
Probability Sampling
  • Stratified sampling
  • Strata formation
  • Purposive
  • Based on past experience
  • Researchers judgement
  • E.g.
  • Regions
  • Sectors
  • Subsectors

15
Probability Sampling
  • Stratified sampling
  • Strata size allocations
  • Proportionate
  • Disproportionate
  • E.g. we want sample size n30
  • Population size N8000
  • Strata is 3 N14000, N22400, N31600
  • Total sample size required is 30
  • Given that size of sample from each strata is
  • n X Pi where i is the strata number
  • P1 4000/8000
  • n1 x P1 30 x 0.5 15
  • Calculate for N22400 and N31600

16
Probability Sampling
  • Cluster Sampling
  • Divide population into subgroups called clusters
  • the subgroups are randomly selected for study
  • All elements of the subgroup is studied.
  • Where subgroups are geographical areas of refer
    to natural boundaries, the cluster sampling
    becomes Area sampling

17
Comparing Stratified and Cluster
  • Stratified
  • Divide population into few groups
  • Each group has many elements
  • Subgroups selected according to variable under
    study
  • Homogeneity within subgroups, heterogeneity b/n
    subgroups
  • Randomly choose elements from subgroup
  • Cluster
  • Divide population into many groups
  • Each with few elements
  • Subgroup selected to ease data collection or
    availability
  • Heterogeneity within subgroups, homogeneity b/n
    subgroups
  • Randomly choose number of subgroups

18
Probability Sampling
  • Cluster Sampling
  • Useful when
  • More economic efficiency than simple random
    sampling is required
  • There is unavailability of practical sampling
    frame

19
Probability Sampling
  • Multi-stage sampling
  • The whole population is divided into subgroups
  • Some subgroups are selected for study
  • This is two-stage sampling
  • If the selection goes beyond this then it is
    three stage
  • Beyond this it is four-stage sampling

20
Probability Sampling
  • Multi-stage sampling
  • Applied in big enquiries extending to a large
    geographical area e.g, the whole of Ghana
  • Advantages
  • Easier to administer since sampling frame is
    developed in partial units
  • The sequential nature enables large areas to be
    covered in bits

21
Probability Sampling
  • Multi-Phase/Sequential sampling
  • collect data from a sample
  • Use information from this sample to select
    subsequent sample

22
Non-Probability Sampling
  • Used for pretest
  • Exploratory studies
  • Convenience
  • Purposive
  • judgemental
  • Quota
  • Snowball

23
Non-Probability Sampling
  • Convenient sampling
  • Unrestricted non-probability design
  • An available sample that which appears able to
    offer answers of interest to a study
  • Least reliable
  • Normally cheapest
  • E.g.

24
Non-Probability Sampling
  • Purposive
  • Conforms to a certain criterion or the studys
    needs
  • Judgemental sampling
  • Quota sampling

25
Non-Probability Sampling
  • Judgemental sampling
  • E.g. Testing a new product, staff are used to try
    the product before the public.
  • Quota
  • Allocate proportional representation
  • E.g. population with 55 male and 45 female.
    Then distribute sample between the sexes to cover
    the sexes.
  • Calculate quota for males for sample size of 60.
    How many males should be sampled

26
Non-Probability Sampling
  • Snowball
  • Identify one respondent who reefers the
    researcher to the next respondent
  • It is like a referral network
  • Used for hard to reach respondents
  • E.g. Prostitutes, Drug pushers, armed robbers
    etc.
  • Similar to reverse literature search

27
Methods of Data Collection
  • Census
  • Survey
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