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Sampling Strategy When Within the scope of each data collection Goal Samples save time and effort when data is collected · when it is impractical, impossible or too expensive to collect all data · when the data collection is a Cumbersome process Deriving a sampling strategy, which provides the most accurate level of information about the population being measured.so objective is to meet the goals of data collection but optimize the effort and cost The sampling strategy comprises the methodology for selecting samples as well as planning the sample size. This basic procedure can be divided into four phases : 1.The selection of samples should be entirely random 2.Choose a selection principle and a selection type different types of selection and selection principles will be driven by cost and effort criteria they vary depending on the question being asked 3.Determine a selection technique in case of random selection Non-random Selection Random Selection Quota Procedure Guideline of quotas e.g. accident repair Application: If only targeted information is needed Simple Sample All units have the same chance of being drawn Advantage: No knowledge about population necessary Disadvantage: High effort Cut-off Procedure Only a part of the population is observed, e.g. accident damage Application: If only one aspect is to be examined Cluster Sample The population is clustered in a logical way and one cluster is selected e.g. sites Advantage: Lower costs Disadvantage: Information can get Lost Haphazard Selection Example: Only the information which can be obtained easily, is collected Application: If only a first impression is to be gained e.g. for estimation of proportion or standard deviation for more precise sample size calculation Stratified Sample The population is stratified according to relevant criteria, e.g. spray-painting type, machine, location etc. Then a representative sample is removed from each stratum Advantage: Smaller sample Disadvantage: Information on the population must be available to start with 4.Determine the sample size The bigger the sample the greater the validity i.e. the quality of the statistical conclusion about the population One should therefore revert to available data (e.g. from IT systems): The data is treated like sample since the process to be improved hasn't yet been stopped When new data is collected (e.g. manual counting, surveys) an assessment of the cost of collection and desired level of confidence and precision must take place All in all three factors play a role when the sample size is determined: · the required Confidence Level which indicates the likelihood that the population mean lies within the given Confidence Interval. This value is normally a given for any organization e.g. 95% · The granularity is an indication of how precise we want to be and is usually half the width of the Confidence Interval · The costs and the duration of the data measurement increase with the sample size. When the sample sizes are calculated it is important to consider whether the requested precision is worth the costs inc Rules of thumb for sample size · Discrete100, at least 5 per category , Data : Ok/ Not Ok - Continuous - 30