Occurrence Sampling - Mercer University

Occurrence Sampling - Mercer University

Occurrence Sampling Problem: how do you know how much time a particular person, group, or function is spending on any given activity? One solution continuous time study expensive not well suited for nonstandard work Alternatively discrete sampling ISE 311 e.g., How much of a students 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 1

Determining Sample Size Law of diminishing returns Sample size depends on ISE 311 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 2 Sample size example

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 ISE 311 z 2 (1 p ) z 2 p(1 p ) n 2 s p A2 0.3825 0.45 0.5175

3 Sampling design and data collection Overcoming the 3 problems in obtaining a representative sample: 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 defining random sampling times/locations table 10.3, randomness with restrictions pg. 142 (ERGO, Excel) ISE 311 4 Data Gathering

ISE 311 Who & how? person or machine? additional duty for employee or hire temp? automated data collection? level of detail the problem of influence does the presence of the observer affect the actions or performance of the entity being observed? techniques to minimize influence

unobtrusive observation random sample distance, video, etc. communication with the observed 5 Data Analysis & Use Comparing frequency data 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 ISE 311 6 Frequency example 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. ISE 311

7 Data Analysis & Use ISE 311 Other comparison methods 2 (independence) or t-test to test for significant difference in means control charts to test for time (or sequence) effects Purpose of the analysis determine if data should remain stratified or can be combined if no difference, combine data and refer to overall proportions if there is a difference, keep data, analysis, and conclusions separate 8

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