Occurrence Sampling
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Problem: how do you know how much time a particular
person, group, or function is spending on any given
activity?
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One solution – continuous time study
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expensive
not well suited for nonstandard work
Alternatively – discrete sampling
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e.g., How much of a student’s time is spent waiting for a report
to print in the computer lab during ‘peak’ times?
How much of the maintenance technicians’ time is spent
waiting for repair calls?
select random sample of population
record activities at discrete intervals
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Determining Sample Size
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Law of diminishing returns
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Sample size depends on …
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amount of information grows proportionately with the square
root of sample size, n
cost of information grows directly with n
therefore, there will be a sample size beyond which additional
information is not worth the cost of additional study
desired absolute accuracy, A
 note difference between absolute and relative accuracy, s
(estimated) proportion of occurrence, p
desired confidence level, c
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Sample size example
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It is estimated that students in the computer lab must
wait in line for their document to print about 45% of
the time. To justify an additional printer, you wish to
verify that estimate within 15% (relative accuracy)
and with a confidence level of 90%.
Solution,
p = 0.4
A = (0.45)(0.15) = 0.0675
-.0675
+.0675
c = 90%  z = ± 1.64
table 10.1,
pg. 137
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z 2 (1  p) z 2 p(1  p)
n

2
s p
A2

0.3825
0.45
0.5175
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Sampling – design and data collection
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Overcoming the 3 problems in obtaining a
representative sample:
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Define reasonable strata (categories) for data collection
 time of day (morning, afternoon, evening, etc.)
 day of week (or weekend/weekday, week in the month,
etc.)
 gender
 region
 socio-economic status
 level of education / training
 etc. Base sample size on smallest estimated proportion
Randomness
table 10.3,
 defining random sampling times/locations
pg. 142
(ERGO,
 randomness with restrictions
Excel)
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Data Gathering
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Who & how?
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person or machine?
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additional duty for employee or hire temp?
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automated data collection?
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level of detail
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the problem of influence
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does the presence of the observer affect the actions or
performance of the entity being observed?
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techniques to minimize influence
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unobtrusive observation
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random sample
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distance, video, etc.
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communication with the observed
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Data Analysis & Use
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Comparing frequency data
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procedure on pg. 145
Example: is there a difference in number of times there
are students waiting for printouts between morning and
afternoon?
Strata
Times Waiting
Times not Waiting
morning
36
64
afternoon
25
75
na = nb = 100
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Frequency example
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Solution,
1. Smallest of 4 numbers = 25
2. Other number in the column = 36
3. “Observed contrast” = 11
4. from Table 10.4, minimum contrast = ______
5. Compare observed contrast
Answer: Morning is / is not different from afternoon.
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Data Analysis & Use
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Other comparison methods
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χ2 (independence) or t-test to test for significant difference in
means
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control charts to test for time (or sequence) effects
Purpose of the analysis – determine if data should
remain stratified or can be combined
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if no difference, combine data and refer to overall proportions
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if there is a difference, keep data, analysis, and conclusions
separate
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