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Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
Data Collection and Presentation
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Data Collection and Presentation

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  • 1. • • • ’•
  • 2. SOURCES OF DATA• PRIMARY SOURCES• SECONDARY SOURCESMETHODS OF COLLECTING DATA• DIRECT /INTERVIEW• INDIRECT/QUESTIONNAIRE• REGISTRATION• OBSERVATION• EXPERIMENTATIONSAMPLING TECHNIQUES• SLOVIN’S FORMULA• NON – PROBABILITY SAMPLING• PROBABILITY SAMPLING
  • 3. ’ In doing a research, if the population is toobig, a substantial number of samples isacceptable. One way of getting a number ofsamples is by using Slovin’s formula:where n is the sample size N is the population size e is the margin of error
  • 4. MARGIN OF ERROR “e”• The margin of error is a value which quantifies possible sampling errors.• Sampling error means that the results in the sample differ from those of the target population because of the “luck of the draw”
  • 5. •
  • 6. TYPES OF SAMPLING TECHNIQUESNON – PROBABILITY SAMPLING PROBABILITY SAMPLING CONVENIENCE SIMPLE SYSTEMATIC QUOTA STRATIFIED PURPOSIVE CLUSTER
  • 7. • PROBABILITY SAMPLING Samples are chosen in such a way that each member of the population has a known though not necessarily equal chance of being included in the samples.• ADVANTAGES OF PROBABILITY SAMPLING 1. It avoids biases. 2. It provides basis for calculating the margin of error
  • 8. • SIMPLE RANDOM SAMPLING: Samples are chosen at random with members of the population having known or sometimes equal probability or chance of being included in the samples. a) Lottery b) Sampling with the use of Table of Random Numbers• SYSTEMATIC RANDOM SAMPLING: Samples are randomly chosen following certain rules set by the researchers• STRATIFIED RANDOM SAMPLING: This method is used when the population N is too big to handle, thus dividing N into subgroups called STRATA.• CLUSTER SAMPLING: Cluster sampling is sometimes called area sampling because it is usually applied when the population is large. Groups or clusters instead of individuals are randomly chosen.
  • 9. –• NON – PROBABILITY SAMPLING: Each member of the population does not have a known chance of being included in the sample. Instead, personal judgement plays a very important role in the selection.
  • 10. • CONVENIENCE SAMPLING: This type is used because of the convenience it offers to the researcher.• QUOTA SAMPLING: This is very similar to stratified random sampling. The only difference is that the selection of the members of the samples in stratified sampling is done randomly.• PURPOSIVE SAMPLING: Choosing the respondents on the basis of predetermined criteria set by the researcher.

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