sampling and levels of measurement data collection
Post on 21-Dec-2015
235 views
TRANSCRIPT
Sampling terms
Population: all subjects one is interested in. Very large or very small
ElementSample: portion of populationSampling frame: list of people
(elements) in the population
Sampling
Representative sample: if the overall characteristics of the sample approximate the important characteristics of the population
Biased sample: not representativeWhy sample? time and money
Sampling in the U.S.
Literary Digest polls. Accurate until 1936, when Landon was predicted as winner of the presidential election
Reasons: (1) low return rates (2 million out of 10 million) and (2) sampling frame (telephone directories and lists of auto owners)
Poor sampling frames result in bias
Sampling in the U.S.
1948 Gallup poll predicted Dewey would win. Problems: (1) stopped polling in Oct.; (2) quota sampling
Two types of sampling: probability and non-probability sampling
Probability sampling uses the laws of probability, whereas non-probability does not
Probability
p = number of times an event could occur / total number of outcomes.
Can be expressed as a fraction, a %, as chances out of 100, or as a decimal.
P can range from 0 (no probability to 1 (certainty)
Sampling
A sample will be more likely to be representative of a population from which it is selected if all members of the population have an equal chance of being selected in the sample
Sampling
Sampling error: error due to the fact that the sample is not representative
Necessity of a complete sampling frame
Probability sampling
Simple random sampling: (out of a hat, random numbers)
Systematic random sampling: every nth element is cnosen, select first element at random (random start)
Probability sampling
Stratified random sampling 1. Divide sample into subgroups based on
important population characteristics 2. Randomly sample from those subgroups
in proportion to their percentage in the population
Probability sampling
Choice of stratification variables will often depend on what variables are available, and how much is known about the population
This technique most likely to be representative
Non-probability sampling
Probability sampling only works if there is a sampling frame of the population. Sometimes that is not possible (i.e., criminals, drug addicts, etc.)
Nonprobability sampling methods, while running the risk being unrepresentative might be the only option
Non-probability sampling
Convenience: the captive audience College students and prisoners
Purposive: researcher uses judgment For example, the mentally ill. Works best if
the criteria for inclusion are clear
Quota: like stratified random. Groups are selected on the basis of known variables
In quota sampling, subjects are not selected randomly--subjects with the desired characteristics are selected until a quota is filled for each subgroup
Tips about sampling
Sample size: unusually the number of subjects needs to be at least 30. If several groups within the sample are to be compared, there needs to be at least 10 per group.
The larger the number of subjects (N), the less likely sampling error
Tips about sampling
There will always be “mortality”
Samples should be larger to take this into account
Tips about sampling
The greater the heterogeneity of the sample, the larger the sample must be. The less population diversity, the smaller N might be.
N is often determined by time and money factors
Nominal
Nominal: lowest level, simply classifying observations into categories
Categories should be mutually exclusive and exhaustive
Examples: gender, major, religion, state
Nominal (continued)
Numbers assigned to the categories have no numerical meaning. Assign individuals, and report the % falling into each category.
Fewer statistical techniques can be used
Ordinal measurement
Ordinal measurement: one observation represents more of a given variable than another observation
Rankings Newly developed tests
Ordinal (continued)
Ranks tell whether one observation represents more or less than another, but not how much more or less--nothing is known about the exact difference between any two ranks
Rankings of crime seriousness
Interval
Interval: like an ordinal scale, but has equal intervals between the units of measurement. Not only an ordering, but also the same distance or degree of difference between observations
For example, 81 is 1 point away from 80, etc.
Well-developed tests are interval level
With interval measurement, can do addition, subtraction, multiplication and division, more statistical tests
Ratio measurement
Ratio measurement: like interval, with the additional property of a true zero.
An individual could have two or three time as much of a trait as another with ratio measurement
Ratio
Height or weight. A 200 lb person weighs twice as much as a 100 pound person
Not true for interval. For example, no such thing as an IQ of 0, and a person with an IQ of 100 is not twice as smart as someone with an IQ of 50