Although several methods have been developed to allow for the analysis of data in the presence of missing values, no clear guide exists to help family researchers in choosing among the many options and procedures available. We delineate these options and examine the sensitivity of the findings in a regression model estimated in three random samples from the National Survey of Families and Households ( n = 250 -2,000). These results, combined with findings from simulation studies, are used to guide answers to a set of 10 common questions asked by researchers when selecting a missing data approach. Modern missing data techniques were found to perform better than traditional ones, but differences between the types of modern approaches had minor effects on the estimates and substantive conclusions. Our findings suggest that the researcher has considerable flexibility in selecting among modern options for handling missing data.Within the last decade, the practice of analyzing data in the presence of missing values has

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We use data from two national surveys of married individuals—one from 1980 and the other from 2000—to understand how three dimensions of marital quality changed during this period. Marital happiness and divorce proneness changed little between 1980 and 2000, but marital interaction declined significantly. A decomposition analysis suggested that offsetting trends affected marital quality. Increases in marital heterogamy, premarital cohabitation, wives' extended hours of employment, and wives' job demands were associated with declines in multiple dimensions of marital quality. In contrast, increases in economic resources, decision‐making equality, nontraditional attitudes toward gender, and support for the norm of lifelong marriage were associated with improvements in multiple dimensions of marital quality. Increases in husbands' share of housework appeared to depress marital quality among husbands but to improve marital quality among wives.

Research linking basal cortisol levels with internalizing and externalizing behavior problems in youths has yielded inconsistent results. We hypothesize that the high moment to moment variation in adrenocortical activity requires an analytical strategy that separates variance in cortisol levels attributable to "stable traitlike" versus "state or situationally specific" sources. Early morning saliva samples were obtained from 724 youths~M age ϭ 13.5 years; range ϭ 6-16 years in Year 1! on 2 successive days 1 year apart. Latent state-trait modeling revealed that 70% of the variance in cortisol levels could be attributed to statelike sources, and 28% to traitlike sources. For boys only, higher levels of externalizing problem behaviors were consistently associated with lower cortisol attributable to traitlike sources across 3 years of behavioral assessment. The inverse association between individual differences in cortisol and externalizing problem behavior has previously only been reported in studies of at-risk or clinical groups. The present findings suggest the relationship is a stable phenomenon that spans both normative and atypical child development. Studies are needed to reveal the biosocial mechanisms involved in the establishment and maintenance of this phenomenon, and to decipher whether individual differences in this hormone-behavior link confers risk or resilience.

Study of the effect of transitions on individual and family outcomes is central to understanding families over the life course. There is little consensus, however, on the appropriate statistical methods needed to study transitions in panel data. This article compares lagged dependent variable (LDV) and change score (CS) methods for analyzing the effect of events in two-wave panel data. The methods are described, and their performances are compared both with a simulation and a substantive example using the National Survey of Families and Households two-wave panel. The results suggest that CS methods have advantages over LDV techniques in estimating the effect of events on outcomes in two-wave panel data.

Although a higher level of psychological distress has been found in many studies of divorced compared with married individuals, explanations for this difference remain elusive. Three basic theoretical explanations have been proposed. Social role theory maintains that the role of being divorced is inherently more stressful than that of being married; crisis theory attributes the higher stress to role transitions and transient stressors of the disruption process, and social selection theory claims that the higher stress levels among the divorced result from the selection of people with poor mental health into divorce. Some empirical support is available for each of these approaches, but all three have not been tested simultaneously in a longitudinal study. This research empirically evaluates the efficacy of these theories in a pooled time-series analysis of a four-wave panel of married persons followed over 12 years. The pooledtime series random effects model was used to es-

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Using pooled time series with random and fixed effects regression models, we examine the effect of age, period, and family life course events on a measure of religious influence on daily life in a panel of 1,339 adults interviewed three times between 1980 and 1992. The results showv a significant, non-linear increase in religiosity with age, with the greatest increase occurring between ages 18 and 30. We also found a significant decline in religiosity between 1980 and 1988, but no evidence of a period effect between 1988 and 1992. Comparison of fixed and random effects solutions found little evidence that a cohort effect accounted for the age findings. The age effect was significantly stronger for Catholics than Protestants and the lower religiosity of males was also significantly stronger for Catholics. Adding children in the range from age two to ten significantly increased religiosity, but family life course events accounted for little if any of the age effect. Prior research demonstrates substantial ambiguity regarding the extent and degree of an age effect on religiosity, the degree to which this effect reflects aging, family life cycle, or period processes, and the extent to which research findings are affected by measurement and study design. We provide additional empirical evidence on these issues by conducting random effects and fixed effects pooled-time series analyses of multiwave panel data extending over a twelve year period. Although no single technique can resolve the ageperiod-cohort riddle, this model allows us to estimate age effects controlling for cohort effects and to estimate possible period effects between 1980 and 1992. BACKGROUND The observed cross-sectional relationships between-age and religiosity are generally explained by one of three theoretical processes. The "traditional model' (Bahr 1970) focuses on developmental processes related to age per se. Alternatively, a life course model (Chaves 1991) attributes change not to developmental processes but to correlated changes in social roles, particularly in the family. A third interpretation characterizes observed variations in religiosity by age as a statistical artifact associated either with cohort replacement or period effects. The biggest controversies in studies of contemporary religiosity have been reserved for the study of period effects, specifically secularization (e.g., Chaves 1991; Firebaugh and Harley 1991; Hout and Greeley 1990). Chaves (1989) concludes there is no age effect on church attendance in the 1972-6 period, but most scholars argue that the major processes t Amy Argue i.s a doctoral candidate in the