## Book Review: Regression Analysis: A Constructive Critique. Advanced Quantitative Techniques in the Social Sciences Series 11, by Richard A. Berk

February 7, 2010## Book Review: Regression With Social Data: Modeling Continuous and Limited Response Variables, by Alfred DeMaris

February 7, 2010## A Conceptual Framework for Ordered Logistic Regression Models

February 7, 2010“Andrew S. Fullerton” : A Conceptual Framework for Ordered Logistic Regression Models, Sociological Methods & Research 2009 38: 306-347.

Ordinal-level measures are very common in social science research.^{ }Researchers often analyze ordinal dependent variables using^{ }the proportional odds logistic regression model. However, Read the rest of this entry »

## Question Order and Interviewer Effects in CATI Scale-up Surveys

February 7, 2010**Silvia Snidero, Federica Zobec, Paola Berchialla, Roberto Corradetti, and Dario Gregori Question Order and Interviewer Effects in CATI Scale-up Surveys**** Sociological Methods & Research 2009 38: 287-305.**

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, Read the rest of this entry »

## A Coefficient of Association Between Categorical Variables With Partial or Tentative Ordering of Categories

February 7, 2010**Volkert Siersma and Svend Kreiner: A Coefficient of Association Between Categorical Variables With Partial or Tentative Ordering of Categories**** Sociological Methods & Research 2009 38: 265-286.**

Goodman and Kruskal’s 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 Read the rest of this entry »

## Is Optimal Matching Suboptimal?

February 7, 2010**Matissa Hollister Is Optimal Matching Suboptimal?**** Sociological Methods & Research 2009 38: 235-264.**

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 authoridentifies a flaw in OM ‘‘indel costs’’^{ }and proposes a solution Read the rest of this entry »