ecmt lecture 07 - Lecture7 Sampling& Sampling Distributions

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
Lecture 7 Sampling Sampling Distributions Distributions
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Determine when to use sampling instead of a  census Distinguish between random and nonrandom  sampling Decide when and how to use various sampling  techniques Be aware of the different types of error that can  occur in a study Understand the impact of the Central Limit Theorem  on statistical analysis Learning Objectives x Use the sampling distributions of
Background image of page 2
Reasons for Sampling Sampling can save money Sampling can save time For given resources, sampling can  broaden the scope of the data set Because the research process is  sometimes destructive, the sample can  save product If accessing the population is impossible,  sampling is the only option
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Reasons for Taking a  Census Eliminate the possibility that a random  sample is not representative of the  population The person authorising the study is  uncomfortable with sample information  
Background image of page 4
Population Frame A list, map, directory, or other source  used to  represent the population Overregistration  —  the frame contains all  members of the target population and some  additional elements Example:  using the chamber of commerce  membership directory as the frame for a  target population of member businesses  owned by women Underregistration  —  the frame does not contain  all members of the target population Example:  using the chamber of commerce  membership directory as the frame for a  target population of all businesses
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Random Versus  Nonrandom Sampling Random sampling Every unit of the population has a known  probability of being included in the sample A chance mechanism is used in the selection  process Eliminates bias in the selection process Also known as probability sampling Nonrandom Sampling Every unit of the population does not have the  same probability of being included in the sample Open to selection bias Not appropriate data collection methods for most  statistical methods Also known as nonprobability sampling
Background image of page 6
Random Sampling  Techniques Simple Random Sample Stratified Random Sample Proportionate Disproportionate Systematic Random Sample Cluster (or Area) Sampling
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Simple Random Sample Every unit of the population has the same  probability of being sampled  Number each frame unit from 1 to N Use a random number table or a random  number generator to select n distinct  numbers between 1 and N, inclusively Easier to perform for small populations Cumbersome for large populations
Background image of page 8
Simple Random Sample: Numbered Population Frame
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 10
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 11/06/2011 for the course ECMT 1010 taught by Professor Vadimtimovsky during the Three '10 term at University of Sydney.

Page1 / 36

ecmt lecture 07 - Lecture7 Sampling& Sampling Distributions

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online