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Descriptive Research. Week 8 Lecture 2. Agenda. Basic sampling concepts Probability sampling Non-probability sampling. Sampling in everyday life. Eating at one of a chain of restaurant => All the restaurant in the chain serve poor food Decision on taking some course

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descriptive research

Descriptive Research

Week 8 Lecture 2

agenda
Agenda
  • Basic sampling concepts
  • Probability sampling
  • Non-probability sampling

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

sampling in everyday life
Sampling in everyday life
  • Eating at one of a chain of restaurant
    • => All the restaurant in the chain serve poor food
  • Decision on taking some course
    • <= opinions of friends who have already taken it
  • Basic idea behind sampling
    • We seek knowledge or information about a whole class of similar objects or events (population)
    • We observe some of these (sample)
    • We extend our findings to the entire class
  • What’s the problem?

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

why sample
Why sample?
  • Heterogeneity of target population (study only one or two cases is not practicable)
  • Save time and money (study of whole population is not possible)
  • Samples may be more accurate than censuses

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

sampling stages
Sampling stages
  • Define the population
    • Target population: specific pool of cases researchers want to study.
    • Example:
      • All full-time students enrolled in university of Sydney between 1999 and present.
      • All admissions to public or private hospitals in Sydney between December 2000 and December 2003
  • Identify the sampling frame
    • The list(s) from which you draw a sample
    • Sampling frame may not reflect the population perfectly
  • Select a sampling procedure
    • Probability/Non-probability
  • Determine the sample size
  • Select the sample units

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

simple random sample
Simple random sample
  • Each unit in the population has an equal chance of being included
  • Obtain a complete list of population
  • Traditional way:
    • Number the units, use a table of random number
  • Random-Digit-Dial (RDD)
  • Simple random sample by excel

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

simple random sample by excel
Simple random sample by excel

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

stratified random sampling
Stratified random sampling
  • The target population is divided into two or more mutually exclusive segment (strata)
  • A simple random sample of units is chosen independently from each stratum
  • Why stratify?
    • You will be able to talk about subgroups (stratum)
    • Every stratum gets a better representation
    • Give higher precision with the same sample size
  • How to form strata?

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

stratified sampling illustration
Stratified sampling illustration

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

proportional stratified sampling
Proportional stratified sampling

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

cluster sampling
Cluster sampling
  • The target population spread out over a larger area.
    • is broken down into mutually exhaustive subsets (clusters)
    • Natural groupings, universities, countries, states, cities, blocks
  • A random sample of clusters are selected
    • One stage
    • Two stages
  • Stratum is homogeneous, cluster should be as heterogeneous as possible
  • Do not need a complete frame of the population
  • Cost saving

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

cluster sampling illustration
Cluster sampling-- illustration

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

systematic sampling
Systematic sampling
  • Select every nth unit after a random start
  • Units in the population can be ordered in some way
    • A telephone list, student list
    • Houses that are ordered along a road
    • Customers who walk one by one through an entrance
  • Advantage: a frame is not always needed

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

systematic sampling illustration
Systematic sampling -- illustration

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

mini workshop
Mini workshop
  • Suppose you would like to do a survey of students on your univerisity to find out how much time on the average they spend studying per week. You obtain from the registrar a list of all students currently enrolled and draw your sample from this list.
    • What is your target population?
    • What is your sampling frame?
    • Assume that the registrar’s list also contains information about each student’s major, year of enrollment, campus of study. How might you obtain a stratified random sample? How might you obtain a cluster sample?

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

non probability sampling
Non-probability sampling
  • Convenience sampling
    • Select a sample from units that are conveniently available
  • Judgment sampling (purposive sampling)
    • Chose units because of certain characteristics
  • Quota sampling
    • Make sure that certain subgroups of units are represented in the sample in approximately the same proportions as they are represented in the population.

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney

determine the sample size
Determine the sample size
  • Make assumptions about the population and use statistical equations to compute the sample size
  • Rule of thumb
    • For small populations (N<100) survey the entire population
    • N is around 500, 50% of the population should be sampled
    • N is around 1500, 20% should be sampled
    • Beyond a certain point (N >= 5000), the population size is almost irrelevant, set sample size to 400.

ISYS3015 Analytical Methods for IS Professionals

School of IT, The University of Sydney