1 / 19

LIS 570

LIS 570. Selecting a Sample. Summary. Sampling - the process of selecting observations random; non-random probability; non-probability. You don’t have to eat the whole ox to know that the meat is tough. Aim. A representative sample A sample which accurately reflects its population

jorgej
Download Presentation

LIS 570

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. LIS 570 Selecting a Sample

  2. Summary • Sampling - the process of selecting observations • random; non-random • probability; non-probability You don’t have to eat the whole ox to know that the meat is tough

  3. Aim • A representative sample • A sample which accurately reflects its population • Avoiding bias

  4. Basic terminology • Population - the entire group of objects about which information is wanted • Unit - any individual member of the population • Sample - a part or subset of the population used to gain information about the whole • Sampling frame - the list of units from which the sample is chosen • Variable - a characteristic of a unit, to be measured for those units in the sample

  5. Step 1: Identify the Population • The units of analysis about whom or which you want to know • Define the population concretely • Example • Adult Residents of Seattle

  6. 2.Decide on a Census or a Sample • Census • Observe each unit • an “attempt” to sample the entire population • not foolproof • Sample • observe a sub-group of the population

  7. 3. Decide on Sampling Approach Random Non-random Probability Non-probability

  8. Random sampling • Random (Probability) Sampling • Each unit (element) has the same chance (probability) of being in the sample • Chance or luck of the draw determines who is in the sample (Random)

  9. Random samples • Each unit has a known probability or chance of being included in the sample • An objective way of selecting units • Random Sampling is not haphazard or unplanned sampling

  10. Types of random sampling • Simple random sample • Systematic sampling • Stratified sampling • Cluster sampling

  11. How to choose The nature of the research problem Availability of a sampling frame Money Desired level of accuracy Data collection method

  12. Simple random samples • Obtain a complete sampling frame • Give each case a unique number starting with one • Decide on the required sample size • Select that many numbers from a table of random numbers • Select the cases which correspond to the randomly chosen numbers

  13. Systematic sampling • Sample fraction • divide the population size by the desired sample size • Select from the sampling frame according to the sample fraction • e.g sample faction = 1/5 means that we select one person for every five in the population • Must decide where to start

  14. Stratified sampling • Premise - if a sample is to be representative then proportions for various groups in the sample should be the same as in the population • Stratifying variable • characteristic on which we want to ensure correct representation in the sample • Order sampling frame into groups • Use systematic sampling to select appropriate proportion of people from each strata

  15. Cluster sampling • Involves drawing several different samples • draw a sample of areas • start with large areas then progressively sample smaller areas within the larger • Divide city into districts - select SRS sample of districts • Divide sample of districts into blocks - select SRS sample of blocks • Draw list of households in each block - select SRS sample of households

  16. Random Samples • Advantages • Ability to generalise from sample to population using statistical techniques • Inferential statistics • High probability that sample generally representative of the population on variables of interest

  17. Non-random Samples • Purposive • Quota • Accidental • Generalizability based on “argument” • Replication • Sample “like” the population

  18. Selecting a sampling method • Depends on the population • Problem and aims of the research • Existence of sampling frame

  19. Conclusion • The purpose of sampling is to select a set of elements from the population in such a way that what we learn about the sample can be generalised to the population from which it was selected • The sampling method used determines the generalizability of findings Random samples X Non-random sample

More Related