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Probability Proportionate to Size


probability-proportionate-to-size.gifThe distribution of companies by R&D in the first partitioned group or by payroll in the third partitioned group was skewed as in earlier frames. Because of this skewness, a fixed sample probability proportionate to size (pps) method remained the appropriate selection technique for these partitioned groups. That is, with the pps method large companies had higher probabilities of selection than did small companies. The fixed sample size methodology has been replicated for every survey year since the 1998 survey.

Companies in the first partitioned group received a measure of size equal to the most recent reported positive R&D expenditure. Companies in the third partitioned group received a measure of size equal to their company payroll. RSE constraints by industry and by state were imposed separately in the first and third partitioned groups and the company received a probability of selection for each industry in which it had activity, as well as each state. The company’s final probability was the maximum of these industry and state probabilities.
Simple Random Sampling

The second partitioned group was split into two strata, manufacturing and nonmanufacturing. Each stratum was sampled using simple random sampling (srs). The use of srs implied that each company within a stratum had an equal probability of selection. Companies in the manufacturing stratum received a probability of selection of roughly 0.01. Companies in the nonmanufacturing stratum received a probability of selection of roughly 0.004.
Sample Stratification and Relative Standard Error Constraints

The particular sample selected was one of a large number of samples of the same type and size that by chance might have been selected. Statistics resulting from the different samples would differ somewhat from each other. These differences are represented by estimates of sampling error or variance. The smaller the sampling error, the less variable the statistic. The accuracy of the estimate, that is, how close it is to the true value, is also a function of nonsampling error.

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