Sociological Methods & Research

Optimal matching (OM) is a method for measuring the similarity between pairs of sequences (e.g., work histories). This article discusses two problems with optimal matching. First, the author identifies a flaw in OM ‘‘indel costs’’ and proposes a solution to this flaw. Second, the author discusses the need for benchmarks to measure the added value of OM and to test competing versions. To that end, the author conducts an empirical test of traditional OM, the alternative localized OM, and sequence comparison. The test documents the problem with traditional OM and shows that it is solved by localized OM. The test also demonstrates the value of OM and sequence comparison in examining occupational sequences; both methods capture variation beyond traditional human capital and status attainment measures, although the marginal improvements of OM over sequence comparison may not justify its computational complexity. These results point to the need for more systematic approaches to sequence analysis methods.

Goodman and Kruskal’s γ coefficient measuring monotone association and its partial variants are useful for the analysis of multiway contingency tables containing ordinal variables. When the categories of a variable are only partly ordered and the variable is treated as a nominal variable, information in the ordering of the categories and statistical power is lost. The authors suggest a ^Pγ measure that is the maximum of the ordinary γ coefficients obtained by permuting the categories of nominal or partially ordered variables, while leaving the partial ordering intact. When the assumption of a monotone underlying association is justified, this measure has higher power than nominal tests for association. Furthermore, the resulting optimal monotone ordering gives insight into the nature of this association, which is not obtained by tests for nominal variables. The properties of the ^Pγ coefficient are investigated in a simulation study and its use illustrated in two data sets.

The scale-up estimator is a network-based estimator for the size of hidden or hard to count subpopulations. Several issues arise in the public health context when the aim is the estimation of injuries occurring in a certain population, where two common problems are present: (a) Small injuries are usually difficult to observe and rarely reported in the official data and (b) people are not always compliant in giving information about some specific injuries, in particular when children are involved. This study checked the methodological issues arising from using a computer-assisted telephone interview (CATI) survey using the scale-up methodology for detecting the number of injuries due to choking in children ages 0 to 14 in Italy. For this purpose, 1,000 CATI interviews were conducted during a week using a questionnaire based on 33 questions about populations of known size according to census data. Then, each respondent was asked about other questions related to the main target population (e.g., number of children known to suffer from a choking accident). A sensitivity analysis was conducted for estimating the effect of varying subpopulations, order of the questions, and interviewer effects on the resulting estimates. For the interviewer effect, no particular differences were observed in the overall estimates of injuries. The conclusion is the scale-up estimator in association with CATI methodology shows a high potential in the field of injury prevention, being accurate and robust, but particular attention should be given to the training of the interviewers to improve stability of the estimates.

Ordinal-level measures are very common in social science research. Researchers often analyze ordinal dependent variables using the proportional odds logistic regression model. However, this ‘‘traditional’’ method is one of many different types of logistic regression models available for the analysis of ordered response variables. In this article, the author identifies 12 distinct models that rely on logistic regression and fit within a framework of three major approaches with variations within each approach based on the application of the proportional odds assumption. This typology provides a degree of conceptual clarity that is missing in the extant literature on logistic regression models for ordinal outcomes. The author illustrates the similarities and differences among the different models with examples from the General Social Survey and the American National Election Study.