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The objective of this research is to examine, validate, and recommend techniques for handling the problem of missingness in observational data. We use a rich observational data set, the Nielsen HomeScan data set, which allows us to effectively combine elements from simulated data sets: large numbers of observations, large number of data sets and variables, allowing elements of “design” that typically come with simulated data, and its observational nature. We created random 20% and 50% uniform missingness in our data sets and employed several widely used methods of single imputation, such as mean, regression, and stochastic regression imputations, and multiple imputation methods to fill in the data gaps. We compared these methods by measuring the error of predicting the missing values and the parameter estimates from the subsequent regression analysis using the imputed values. We also compared coverage or the percentages of intervals that covered the true parameter in both cases. Based on our results, the method of single regression or conditional mean imputation provided the best predictions of the missing price values with 28.34 and 28.59 mean absolute percent errors in 20% and 50% missingness settings, respectively. The imputation from conditional distribution method had the best rate of coverage. The parameter estimates based on data sets imputed by conditional mean method were consistently unbiased and had the smallest standard deviations. The multiple imputation methods had the best coverage of both the parameter estimates and predictions of the dependent variable.

Past research has established a relationship between the perceptions of fairness in the division of household labor and relationship satisfaction. Varying according to gender and time, this relationship has been found with differing outcomes, including relationship satisfaction, relationship happiness, divorce, and sexual frequency. Although this relationship has been well studied, little research has focused on how this relationship is moderated by relationship status. According to the Second Demographic Transition Theory (SDT), as societies become more “modern,” cohabitation will become more prevalent, eventually becoming socially and culturally equivalent to marriage. As such, it is vital to ask how cohabitation and marriage differ, or if they differ at all. Therefore, this gap is explored by asking, “How do perceptions of the division of household labor affect married and cohabitating heterosexual couples’ relationship happiness and chance of separation?” In order to answer this question, the National Survey of Families and Households (Wave III) is analyzed, with outcomes focusing on relationship happiness and chance of separation. Results indicate that when married and cohabitating individuals experience similar levels of happiness with their partner’s housework, they also experience similar levels of relationship happiness and chance of separation, with relationship status not affecting the impact happiness with partner’s housework has on these relationship outcomes. This suggests that cohabitation and marriage may continue to become more similar overall.

Missing data are a pervasive problem in special education research. The purpose of this chapter is to provide researchers with an overview of two “modern” alternatives for handling missing data, full information maximum likelihood (FIML) and multiple imputation (MI). These techniques are currently considered to be the methodological “state of the art”, and generally provide more accurate parameter estimates than the traditional methods that are still common in published educational studies. The chapter begins with an overview of missing data theory, and provides brief descriptions of some traditional missing data techniques and their requisite assumptions. Detailed descriptions of FIML and MI are given, and the chapter concludes with an analytic example from a longitudinal study of depression.

A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is considerably faster and easier to use than the leading methods recommended in the statistics literature. It shows through a set of simulations that the Lerp works well, whereas other existing methods fail to perform properly, when the panel data contain a high degree of missingness and/or a strong correlation across cross-sectional units. As an illustration, the method is applied to study the cost-of-living-index dataset with missing values. The test on the imputed panel data provides the supporting evidence for the U.S. economy convergence that depends on the state physical spatial proximities and the state industrial development similarities.

In 2014, the Colombian Government commissioned a unique national survey on illegal liquor. Interviewers purchased bottles of liquor from interviewees and tested them for authenticity in a laboratory. Two factors predict whether liquor is contraband (smuggled): (1) the absence of a receipt and (2) the presence of a discount offered by the seller. Neither factor predicts whether a bottle is adulterated. The results back a story in which sellers are complicit with a contraband economy, but whether buyers are complicit remains unclear. However, buyers are more likely to receive adulterated liquor when specifically asking for a discount.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

There is a substantial body of research on the calculation of the costs of conflict, but so far no satisfactory methodology has been proposed that is able to combine all potential channels in one single analysis. This chapter uses the existing literature and its problems to propose a methodology for doing so.

The specific problems addressed in this study include the measurement of welfare, the imputation of missing data, the validity of the econometric techniques used in the estimation of conflict costs, the differentiation of existing conflict databases, and the possibility of both direct and nondirect effects. These challenges are described in detail in this chapter and a comprehensive methodological road map is proposed to be able to estimate the global economic costs of conflict. This contribution is an important continuation of our research agenda with regard to the calculation of the global economic costs of conflict.

This chapter proposes a simple procedure to estimate average derivatives in nonparametric regression models with incomplete responses. The method consists of replacing the responses with an appropriately weighted version and then use local polynomial estimation for the average derivatives. The resulting estimator is shown to be asymptotically normal, and an estimator of its asymptotic variance–covariance matrix is also shown to be consistent. Monte Carlo experiments show that the proposed estimator has desirable finite sample properties.

Drawing from theories of modernization and socioemotional selectivity, this study investigates the effect of familial support on the relationship between immigrant generation and mental health service use for Asian American and Latinx older adults.

Using the data from the National Latino and Asian American Study (NLAAS) 2002–2003, nested logistic regressions (N = 810) were used to test the effects of familial support (parent–child relationship quality) on the relationship between immigrant generation and the use of mental health services. Differences in familial support between older adults and their younger counterparts were also accounted for.

The results indicate that familial support partially attenuates the relationship between immigrant generation and mental health service use, but only for Latinx groups. Familial support was not significantly different for older adults than that of those younger in age.

Findings suggest the need for a better understanding of familial support as it relates to mental health service use for these groups. Approaches to improving the access to, and the overall use of, mental health services should be sensitive to ethnic variation. Immigrant groups may also endure stressors associated with legal and citizenship status. Future research should consider the effect of these political identities on mental health. Studies on parent–child relationship quality should also be longitudinal in order to better understand the dynamic nature of familial support across the life course.

This chapter addresses gaps in the literature as Asian Americans are relatively understudied group with regard to mental health. Previous studies showed that US-born Asian American and Latinx populations are more likely to use mental health services than their foreign-born counterparts, but the effects of generation status and familial support for older adults are unclear.

Some students with documented learning needs (e.g., learning disabilities, physical challenges) receive strong support through the legislation, funding, and accountability systems associated with the Individuals with Disabilities Education Act (IDEA, 2004) and Section 504 of the Rehabilitation Act (1973). However, in the absence of supportive federal policy, other students with documented learning needs (e.g., high cognitive ability) experience varying levels of support due to differences in state and local policies, funding, and accountability requirements. These differences are due in large part to misconceptions about students with advanced learning needs (e.g., that they can meet grade-level standards without intervention) and equity concerns (e.g., students with the greatest perceived needs should be served first). Special education has a long history of alleviating educational mismatches by preparing students for challenging learning opportunities, providing classroom support structures, and monitoring educational placements through a system of regular evaluation and adjustment. Students served in gifted and talented education can benefit from these same asset-based, sociocultural approaches. However, efforts to support students with advanced learning needs are more likely to be consistently and successfully applied if they are backed by changes to existing policies, funding, and accountability systems.