No abstract

To explain changes in the sociometric configuration of a group through time, a problem arises of the extent to which such changes may be viewed as the aggregation of part-processes occurring at the level of two-person choice structures. A possible model is a Markov chain in which three possible states are mutual choice, one-way choice, and indifference, one realization for each pair of choosing individuals in the group. Choice data for an eighth-grade classroom are fitted to this model and are used to answer questions of constancy of transition probabilities, order of the chain, and sex differences.

The misclassification process is represented by a stochastic matrix containing the probabilities that an individual who belongs in one cell is counted as belonging to another (or perhaps the same) cell. These probabilities are supposed known. If misclassification in the row direction is independent of that along the column variable then the size of the usual chi-square test is unchanged. It is shown how to calculate loss of power in this case and also how to calculate the change in size of the test if the errors are not independent. A modified test criterion is suggested when errors are not independent and a numerical example is included.

No abstract

A method is presented to determine which of a set of sampling techniques is most nearly optimum for sampling weed abundance. To employ the method, one needs first to develop a set of sampling alternatives. A pilot survey is then conducted to determine time costs and sampling and measurement error variances associated with each technique. We used this method to determine which of three plot sizes (0.6 by 0.6 m, 0.6 by 1.5 m, 1.2 by 2.1 m) and two types of orchard scans is most nearly optimum for estimating weed abundance in apple [Malus domestica (Bork)] orchards. The results indicate that, for weed species that are fairly apparent, orchard scans are optimal when less than 8 h of sampling effort is expended, but when a high degree of precision is required, or when the weed species is more difficult to detect, plot samples may be the better choice. A size of plot ca. 0.6 by 1.5 m size was found to be most nearly optimum.