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Data relating to 526 homeless families in temporary accommodation were collected in a study directed mainly to the examination of support networks of a sample of these families. The data, however, have allowed the authors to examine the effects of certain characteristics of these families on the average length of stay in temporary accommodation before they were housed permanently. Log-linear analysis was used to establish the independent effect of various attributes available from the data. In general the feature which seemed to influence the length of stay most was the time of the year when the family was admitted into temporary accommodation followed by the sort of reason the families gave for being homeless at the time of admission. Those who had been illegally evicted tended to take longer to be rehoused whilst those with rental or tenant difficulties took less time to be rehoused. The authors are then able to produce profiles based on this analysis of those families which are likely to stay longest in temporary accommodation as against those likely to stay the shortest time.

The article discusses survey techniques and quantitative analysis in the prosecution of art as against observation and verbal types of analysis. The foundation of statistical inference from samples representative of a wider population has now become commonplace knowledge and most first year students in the social sciences are made familiar with such notions as 'a representative sample', 'sampling error', 'biased and unbiased estimates' and similar ideas developed to express the logic of making inferences about a larger population from a considerably smaller sample. The method of case studies is, of course, general and has been extensively used, for example, in political science and in sociology. The argument that has been advanced here, then, is that case studies of whatever form are a reliable and respectable procedure of social analysis and that much criticism of their reliability and validity has been based on a misconception of the basis upon which the analyst may justifiably extrapolate from an individual case study to the social process in general.

The article focuses on the extent to which people undergoing the crisis of homelessness were able to secure personal support from others to whom they were connected. The significance of interpersonal relationships in homelessness is indicated by the fact that such an enormous proportion of those applying for aid from the Housing Department had instanced "disputes with spouses or relatives" as the reason why they were homeless at the time that they applied for help. It should not be assumed, of course, that the reason given to an administrative officer at the time of interview is a reliable indicator of the "cause" of homelessness. Issues such as unemployment, poverty and similar structural circumstances were usually crucial elements in the situation. Disputes with a spouse or kinsperson were rather the precipitating, and no doubt the most immediately influential factor from the point of view of the person applying for aid. The procedure adopted was to note carefully the identities of all people mentioned by the women in their first interview, when they were questioned about their family, housing histories, employment histories and the factors which had led to their becoming homeless.

This article focuses on the calculation of predicted correlations from path coefficients. Tracing the paths in a casual diagram in order to calculate predicted correlation between variable can be tiresome and confusing procedure. If the causal structure among a set of variables is known, and the values of the path coefficients determined, the set of correlations expected among the variables can be calculated as a routine matter very simply on a computer. The procedure derives from the equations linking path coefficients and correlation. The predicted correlations, therefore, may be calculated from the coefficients in a series of steps where the values calculated at one step are used to derive the values of the predicted correlations at the next step. If the initial variables are unordered then the correlations between the variables are read into the (ij) and (ji) cells with unities, as before, in the cells of the leading diagonals relating to these cells. The products are entered into the appropriate rows and columns of A and until the leading diagonal is filled. Then the columns of A are multiplied by the next row of P until the appropriate cell in the leading diagonal of B is filled.