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Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

In this large study using Medicare data, hospitalization of patients was associated with increased mortality in their wives and husbands, and the adverse effect was independent of the effect of spousal death. The effect varied among illnesses; for example, a man's hospitalization for colon cancer did not increase his wife's risk of death, but his hospitalization for dementia had a substantial effect. Hospitalization of patients was associated with increased mortality in their wives and husbands, and the adverse effect was independent of the effect of spousal death. The health of people connected by a social tie may be interdependent. The effect of the death of one spouse on the risk of death in the other (i.e., the partner) — referred to as the bereavement effect — is the best-documented example of such interpersonal health effects. 1 , 2 The effect of illness in one spouse on the risk of illness in a partner is a further example. The latter phenomenon, sometimes termed caregiver burden, 3 , 4 has typically been studied as if it were unrelated to the bereavement effect. Indeed, most work has previously focused on how spousal illness worsens . . .

Studies examining determinants of divorce have largely ignored differences between factors that elevate wives' and husbands' initiation of divorce. The authors use longitudinal data and a latent class model embedded in a competing-risks event history model to assess distinct predictors of wives and husbands leaving marriages. They find that when men are not employed, either spouse is more likely to leave. When wives report better-than-average marital satisfaction, their employment affects neither spouse's exit. However, when wives report below-average marital satisfaction, their employment makes it more likely they will leave. The authors' findings suggest that theories of divorce require \"gendering\" to reflect asymmetric gender change.

Building on recent research emphasizing how legitimacy depends on consensus among audiences about candidates' characteristics and activities, we examine the relationship between cultural producers' (candidates) position in the social structure and the consecration of their creative work by relevant audiences. We argue that the outcome of this process of evaluation in any cultural field, whether in art or science, is a function of (1) candidates' embeddedness within the field, and (2) the type of audience—that is, peers versus critics—evaluating candidates' work. Specifically, we hypothesize that peers are more likely to favor candidates who are highly embedded in the field, whereas critics will not show such favoritism. We find support for these hypotheses in the context of the Hollywood motion picture industry.

Background Statistical tests of mediation are important for advancing implementation science; however, little research has examined the sample sizes needed to detect mediation in 3-level designs (e.g., organization, provider, patient) that are common in implementation research. Using a generalizable Monte Carlo simulation method, this paper examines the sample sizes required to detect mediation in 3-level designs under a range of conditions plausible for implementation studies. Method Statistical power was estimated for 17,496 3-level mediation designs in which the independent variable ( X ) resided at the highest cluster level (e.g., organization), the mediator ( M ) resided at the intermediate nested level (e.g., provider), and the outcome ( Y ) resided at the lowest nested level (e.g., patient). Designs varied by sample size per level, intraclass correlation coefficients of M and Y , effect sizes of the two paths constituting the indirect (mediation) effect (i.e., X → M and M → Y ), and size of the direct effect. Power estimates were generated for all designs using two statistical models—conventional linear multilevel modeling of manifest variables (MVM) and multilevel structural equation modeling (MSEM)—for both 1- and 2-sided hypothesis tests. Results For 2-sided tests, statistical power to detect mediation was sufficient (≥0.8) in only 463 designs (2.6%) estimated using MVM and 228 designs (1.3%) estimated using MSEM; the minimum number of highest-level units needed to achieve adequate power was 40; the minimum total sample size was 900 observations. For 1-sided tests, 808 designs (4.6%) estimated using MVM and 369 designs (2.1%) estimated using MSEM had adequate power; the minimum number of highest-level units was 20; the minimum total sample was 600. At least one large effect size for either the X → M or M → Y path was necessary to achieve adequate power across all conditions. Conclusions While our analysis has important limitations, results suggest many of the 3-level mediation designs that can realistically be conducted in implementation research lack statistical power to detect mediation of highest-level independent variables unless effect sizes are large and 40 or more highest-level units are enrolled. We suggest strategies to increase statistical power for multilevel mediation designs and innovations to improve the feasibility of mediation tests in implementation research.

Objectives. We assessed the independent effect of homeless and doubled-up episodes on physical and mental health, cognitive development, and health care use among children. Methods. We used data from 4 waves of the Fragile Families and Child Wellbeing Study, involving a sample of 2631 low-income children in 20 large US cities who have been followed since birth. Multivariate analyses involved logistic regression using the hybrid method to include both fixed and random effects. Results. Of the sample, 9.8% experienced homelessness and an additional 23.6% had a doubled-up episode. Housing status had little significant adverse effect on child physical or mental health, cognitive development, or health care use. Conclusions. Family and environmental stressors common to many children in poverty, rather than just homeless and doubled-up episodes, were associated with young children's poor health and cognitive development and high health care use. Practitioners need to identify and respond to parental and family needs for support services in addition to housing assistance to effectively improve the health and development of young children who experience residential instability, particularly those in homeless families.

BACKGROUND:We collected detailed activity paths of urban youth to investigate the dynamic interplay between their lived experiences, time spent in different environments, and risk of violent assault. METHODS:We mapped activity paths of 10- to 24-year-olds, including 143 assault patients shot with a firearm, 206 assault patients injured with other types of weapons, and 283 community controls, creating a step-by-step mapped record of how, when, where, and with whom they spent time over a full day from waking up until going to bed or being assaulted. Case–control analyses compared cases with time-matched controls to identify risk factors for assault. Case-crossover analyses compared cases at the time of assault with themselves earlier in the day to investigate whether exposure increases acted to the trigger assault. RESULTS:Gunshot assault risks included being alone (odds ratio [OR] = 1.6, 95% confidence interval [CI] = 1.3, 1.9) and were lower in areas with high neighbor connectedness (OR = 0.7, 95% CI = 0.6, 0.8). Acquiring a gun (OR = 1.4, 95% CI = 1.1, 1.6) and entering areas with more vacancy, violence, and vandalism (OR = 1.7, 95% CI = 1.1, 2.7) appeared to trigger the risk of getting shot shortly thereafter. Nongunshot assault risks included being in areas with recreation centers (OR = 1.2, 95% CI = 1.1, 1.4). Entering an area with higher truancy (OR = 1.6, 95% CI = 1.1, 2.5) and more vacancy, violence, and vandalism appeared to trigger the risk of nongunshot assault. Risks varied by age group. CONCLUSIONS:We achieved a large-scale study of the activities of many boys, adolescents, and young men that systematically documented their experiences and empirically quantified risks for violence. Working at a temporal and spatial scale that is relevant to the dynamics of this phenomenon gave novel insights into triggers for violent assault.

The authors assessed how the relative age of spouses affects whether men or women initiate a divorce, using data from the National Survey of Families and Households. Ex-spouses' reports of who left generally agreed, but not always, so the analysis used a latent class model embedded in an event-history model with competing risks that the woman leaves the man or the man leaves the woman. Support was not found for the hypothesis that age heterogamy itself increases the odds of divorce: Even large age differences did not make men more likely to leave younger wives, and women's exits were as likely when the marriage is homogamous as when she was older. The main conclusion is that both men and women are more likely to leave if their spouse is older than they are. The effects were stronger for men, but the gender difference in effect size was not statistically significant.

We examine whether having an affair around the time a marriage broke up is associated with being the person who wanted the divorce more or the person who was left. We also examine predictors of having an affair around the end of the marriage. We use the National Survey of Families and Households, using each ex-spouse's reports of which spouse wanted the divorce more and whether either was having an affair around the end of the marriage. We combine latent class models with logistic regression, treating either spouse's report as a fallible indicator of the reality of whether each had an affair and who wanted the divorce more. We find that a spouse having an affair is more likely to be the one who wanted the divorce more. We find little gender difference in who has affairs preceding divorce. Results suggest that it is more common to leave because one is having an affair, or to have an affair because one has decided to leave, than it is to discover one's spouse having an affair and initiate a divorce.