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Variables, sampling, and sample size

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Variables, sampling, and sample size Overview Variables Types of variables Sampling Types of samples Why specific sampling methods are used Variable Anything which ... – PowerPoint PPT presentation

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Title: Variables, sampling, and sample size


1
Variables, sampling, and sample size
2
Overview
  • Variables
  • Types of variables
  • Sampling
  • Types of samples
  • Why specific sampling methods are used

3
Variable
  • Anything which varies and can be measured
  • Variables differ according to definition
  • Categories __________
  • Continuous _________
  • Can you think of any examples ?
  • Specifically, variables represent persons or
    objects that can be manipulated, controlled, or
    merely measured for the sake of research.
  • Variation How much a variable varies. Those
    with little variation are called constants.

4
Independent Variable
  • Independent influences the dependent variable
  • more or less controlled.
  • researchers manipulate these variables
  • Often there are many in a given study.

5
Dependent variable
  • not controlled or manipulated, but are measured
  • These vary in relation to the independent
    variables, and while results can be predicted,
    the data is always measured.
  • There can be any number of dependent variables,
    but usually there is one to isolate reason for
    variation.

6
Independent V. Dependent
  • Intentionally manipulated
  • Controlled
  • Vary at known rate
  • Cause
  • Intentionally left alone
  • Measured
  • Vary at unknown rate
  • Effect

7
Measurement of variables
  • Nominal (qualitative, category or categorical
    variable)
  • Two or more named categories
  • Dichotomous friend/anyone else
  • Multinominal more than two

8
Quantitative variables
  • Numbers or values are assigned to each person or
    case represent increasing levels of variables
  • Social class lower 1, middle 2, upper 3
  • Higher values higher social class

9
Example
  • Students of different ages were given the same
    jigsaw puzzle to put together. They were timed
    to see how long it took to complete the puzzle.

10
What was the independent variable?
  • Ages of the students
  • Different ages were tested by the scientist

11
What was the dependent variable?
  • The time taken to put the puzzle together
  • The time was observed and measured by the
    scientist

12
What was a controlled variable?
  • Same puzzle
  • All of the participants were tested with the same
    puzzle.
  • It would not have been a fair test if some had an
    easy 30 piece puzzle and some had a harder 500
    piece puzzle.

13
Universalism
  • Is human behaviour the same ?

For all people For all cultures For all societies
Can Psychologists really make generalisations
about human behaviour
14
Representative and Convenience samples
  • How big should our sample be
  • How should we pick our sample
  • If done effectively both help psychologists
  • to generalise their findings

15
How and Why Do Samples Work?
  • Sample
  • a small collection of units taken from a larger
    collection.
  • Population
  • a larger collection of units from which a sample
    is taken.
  • Random sample
  • a sample drawn in which a random process is
    used to select units from a population
  • These are best to get an accurate representation
    of the population
  • But are difficult to conduct.

16
How and Why Do Samples Work?
17
Four Types Of Non-Random Samples
  • Convenience sampling (opportunistic)
  • Participants are picked due to availability
  • Does not include the whole population as
    potential participants
  • Stopping shoppers (not a true random sample!)
  • Quota sample
  • Pre-set categories that are characteristics of
    the population (gender / age) e.g. 20 1st years
    (10 male 10 female)

18
Four Types Of Non-Random Samples
19
Four Types Of Non-Random Samples
  • Purposive (Judgmental) sampling
  • Researcher picks subjectively and tries to
    include a range between extremes.
  • Snowball (network) sampling
  • Based on connections in a pre-existing network
    i.e. contact a few vegetarians, then ask if they
    know other vegetarians

20
Coming to Conclusions about Large Populations
  • Sampling element
  • a case or unit of analysis of the population
    that can be selected for a sample.
  • Universe
  • the broad group to whom you wish to generalize
    your theoretical results.
  • Population
  • a collection of elements from which you draw a
    sample.

21
Coming to Conclusions about Large Populations
  • Target population 
  • the specific population that you used.
  • Sampling frame
  • a specific list of sampling elements in the
    target population.
  • Population parameter
  • any characteristic of the entire population that
    you estimate from a sample.

22
Coming to Conclusions about Large Populations
  • Sampling ratio
  • the ratio of the sample size to the size of the
    target population.

23
Coming to Conclusions about Large Populations
  • Why Use a Random Sample?
  • Representation.
  • mathematical or mechanical.
  • Allow calculation of probability of outcomes with
    great precision.
  • sampling ratio
  • the ratio of the sample size to the size of the
    target population.
  • Sampling error
  • the degree to which a sample deviates from a
    population.

24
Coming to Conclusions about Large Populations
  • Types of Random Samples
  • Simple Random Samples
  • random number table or computer
  • Sampling distribution
  • A plot of many random samples, with a sample
    characteristic across the bottom and the number
    of samples indicated along the side.

25
Coming to Conclusions about Large Populations
  • Types of Random Samples
  • Systematic Sampling
  • 7000/100 70 (every 70th student on the list)
  • Rnd(70) any number between 1-70
  • Every 70th student after that

26
Coming to Conclusions about Large Populations
  • Types of Random Samples
  • Stratified Sampling
  • a type of random sampling in which a random
    sample is draw from multiple sampling frames,
    each for a part of the population.

27
Coming to Conclusions about Large Populations
28
Coming to Conclusions about Large Populations
  • Types of Random Samples
  • Cluster (multi-stage) sampling
  • a multi-stage sampling method, in which clusters
    are randomly sampled, then a random sample of
    elements is taken from sampled clusters.
  • e.g.3 schools picked, 33 pupils randomly selected
    from each (cluster again year groups)

29
Coming to Conclusions about Large Populations
30
Coming to Conclusions about Large Populations
31
Three Specialized Sampling Techniques
  • Random Digit Dialing
  • Computer based random sampling of telephone
    numbers.
  • Within Household Samples
  • Random sampling from within households.
  • Sampling Hidden Populations
  • Hidden Population
  • A group that is very difficult to locate and may
    not want to be found, and therefore, are
    difficult to sample.

32
Inferences from A Sample to A Population
  • How to Reduce Sampling Errors
  • the larger the sample size, the smaller the
    sampling error.
  • the greater the homogeneity (or the less the
    diversity), the smaller its sampling error.
  • How Large Should My Sample Be?
  • the smaller the population, the bigger the
    sampling ratio must be for an accurate sample.
  • as populations increase to over 250,000, sample
    size no longer needs to increase.
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