Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localize...

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Birds of a Feather: Homophily in Social Networks

计量经济分析 = Econometric analysis

Logistic regression estimates do not behave like linear regression estimates in one important respect: They are affected by omitted variables, even when these variables are unrelated to the independent variables in the model. This fact has important implications that have gone largely unnoticed by sociologists. Importantly, we cannot straightforwardly interpret log-odds ratios or odds ratios as effect measures, because they also reflect the degree of unobserved heterogeneity in the model. In addition, we cannot compare log-odds ratios or odds ratios for similar models across groups, samples, or time points, or across models with different independent variables in a sample. This article discusses these problems and possible ways of overcoming them.

Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It

Data on social networks may be gathered for all ties linking elements of a closed population ("complete" network data) or for the sets of ties surrounding sampled individual units ("egocentric" network data). Network data have been obtained via surveys and questionnaires, archives, observation, diaries, electronic traces, and experiments. Most methodological research on data quality concerns surveys and questionnaires. The question of the accuracy with which informants can provide data on their network ties is nontrivial, but survey methods can make some claim to reliability. Unresolved issues include whether to measure perceived social ties or actual exchanges, how to treat temporal elements in the definition of relationships, and whether to seek accurate descriptions or reliable indicators. Continued research on data quality is needed; beyond improved samples and further investigation of the informant accuracy/reliability issue, this should cover common indices of network structure, address the consequences of sampling portions of a network, and examine the robustness of indicators of network structure and position to both random and nonrandom errors of measurement.

Network data and measurement

The study of race and ethnic conflict historically has been hampered by inadequate and simplistic theories. I contend that the central problem of the various approaches to the study of racial phenomena is their lack of a structural theory of racism. I review traditional approaches and alternative approaches to the study of racism, and discuss their limitations. Following the leads suggested by some of the alternative frameworks, I advance a structural theory of racism based on the notion of racialized social systems. "The habit of considering racism as a mental quirk, as a psychological flaw, must be abandoned." -Frantz Fanon (1967:77) he area of race and ethnic studies lacks a _ sound theoretical apparatus. To complicate matters, many analysts of racial matters have abandoned the serious theorization and reconceptualization of their central topic: racism. Too many social analysts researching racism assume that the phenomenon is selfevident, and therefore either do not provide a definition or provide an elementary definition (Schuman, Steeh, and Bobo 1985; Sniderman and Piazza 1993). Nevertheless, whether implicitly or explicitly, most analysts regard racism as a purely ideological phenomenon.

Rethinking racism: toward a structural interpretation *

This article describes the gologit2 program for generalized ordered logit models. gologit2 is inspired by Vincent Fu's gologit routine (Stata Technical Bulletin Reprints 8: 160–164) and is backward...

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Generalized ordered logit/partial proportional odds models for ordinal dependent variables

Many researchers and journals place a strong emphasis on the sign and statistical significance of effects—but often there is very little emphasis on the substantive and practical significance of th...

/pdf/using-the-margins-command-to-estimate-and-interpret-adjusted-54sb482vxq.pdf

Using the margins command to estimate and interpret adjusted predictions and marginal effects

As Long & Freese show, it can often be helpful to compute predicted/expected values for hypothetical or prototypical cases. Stata 11 introduced new tools for making such calculations â€“ factor variables and the margins command. These can do many of the things that were previously done by Stataâ€™s own adjust and mfx commands, as well as Long & Freeseâ€™s spost9 commands like prvalue. Unfortunately, the complexity of the margins syntax, the daunting 50 page reference manual entry that describes it, and a lack of understanding about what margins offers over older commands may have dissuaded researchers from using it. This paper therefore shows how margins can easily replicate analyses done by older commands. It demonstrates how margins provides a superior means for dealing with interdependent variables (e.g. X and X^2; X1, X2, and X1 * X2; multiple dummies created from a single categorical variable), and is also superior for data that are svyset. The paper explains how the new asobserved option works and the substantive reasons for preferring it over the atmeans approach used by older commands. The paper primarily focuses on the computation of adjusted predictions but also shows how margins has the same advantages for computing marginal effects.

Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects

When outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical method. However, generalized ordered logit/partial proportional odds models (gologit/ppo) are often a superior alternative. Gologit/ppo models can be less restrictive than proportional odds models and more parsimonious than methods that ignore the ordering of categories altogether. However, the use of gologit/ppo models has itself been problematic or at least sub-optimal. Researchers typically note that such models fit better but fail to explain why the ordered logit model was inadequate or the substantive insights gained by using the gologit alternative. This paper uses both hypothetical examples and data from the 2012 European Social Survey to address these shortcomings.

Understanding and interpreting generalized ordered logit models

Allison (1999) notes that comparisons of logit and probit coefficients across groups can be invalid and misleading, proposes a procedure by which these problems can be corrected, and argues that ``routine use [of this method] seems advisable'' and that ``it is hard to see how [the method] can be improved.'' In this article, the author argues that as originally proposed, Allison's method can have serious problems and should not be applied on a routine basis. However, this study also shows that his model belongs to a larger class of models variously known as heterogeneous choice or location-scale models. Several advantages of this broader and more flexible class of models are illustrated. Dependent variables can be ordinal in addition to binary, sources of heterogeneity can be better modeled and controlled for, and insights can be gained into the effects of group characteristics on outcomes that would be missed by other methods.

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Richard Williams

Papers

Generalized ordered logit/partial proportional odds models for ordinal dependent variables

Using the margins command to estimate and interpret adjusted predictions and marginal effects

Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects

Understanding and interpreting generalized ordered logit models

Using Heterogeneous Choice Models to Compare Logit and Probit Coefficients Across Groups