Covariance Models For Latent Structure In Longitudinal Data

We present several approaches to modeling latent structure in longitudinal studies when the covariance itself is the primary focus of the analysis. This is a departure from much of the work on longitudinal data analysis, in which attention is focused solely on the cross-sectional mean and the influence of covariates on the mean. Such analyses are particularly important in policy-related studies, in which the heterogeneity of the population is of interest. We describe several traditional approaches to this modeling and introduce a flexible, parsimonious class of covariance models appropriate to such analyses. This class, while rooted in the tradition of mixed effects and random coefficient models, merges several disparate modeling philosophies into what we view as a hybrid approach to longitudinal data modeling. We discuss the implications of this approach and its alternatives especially on model interpretation. We compare several implementations of this class to more commonly employed mixed effects models to describe the strengths and limitations of each. These alternatives are compared in an application to long-term trends in wage inequality for young workers. The findings provide additional guidance for the model formulation process in both statistical and substantive senses.     

Methods for Handling Missing Secondary Respondent Data

Secondary respondent data are underutilized because researchers avoid using these data in the presence of substantial missing data. The authors reviewed, evaluated, and tested solutions to this problem. Five strategies of dealing with missing partner data were reviewed: (a) complete case analysis, (b) inverse probability weighting, (c) correction with a Heckman selection model, (d) maximum likelihood estimation, and (e) multiple imputation. Two approaches were used to evaluate the performance of these methods. First, the authors used data from the National Survey of Fertility Barriers (n = 1,666) to estimate a model predicting marital quality based on characteristics of women and their husbands. Second, they conducted a simulation testing the 5 methods and compared the results to estimates where the true value was known. They found that the maximum likelihood and multiple imputation methods were advantageous because they allow researchers to utilize all of the available information as well as produce less biased and more efficient estimates.     

Handling Missing Values in Longitudinal Panel Data With Multiple Imputation

This article offers an applied review of key issues and methods for the analysis of longitudinal panel data in the presence of missing values. The authors consider the unique challenges associated with attrition (survey dropout), incomplete repeated measures, and unknown observations of time. Using simulated data based on 4 waves of the Marital Instability Over the Life Course Study (n = 2,034), they applied a fixed effect regression model and an event‐history analysis with time‐varying covariates. They then compared results for analyses with nonimputed missing data and with imputed data both in long and in wide structures. Imputation produced improved estimates in the event‐history analysis but only modest improvements in the estimates and standard errors of the fixed effects analysis. Factors responsible for differences in the value of imputation are examined, and recommendations for handling missing values in panel data are presented.     

Integrating Survey and Ethnographic Methods For Systematic Anomalous Case Analysis

This paper describes how the salience of research findings can be enhanced by combining survey and ethnographic methods to draw insights from anomalous cases. Using examples from a research project examining the influence of religion on childbearing preferences in Nepal, the author illustrates how survey data can facilitate the selection of ethnographic informants and how semistructured interviews with these deviant cases leads to improved theory, measures, and methods. A systematic sample of 28 informants, whose family size preferences were much larger than a multivariate regression model predicted, were selected from the survey respondent pool for observation and in–depth interviews. The intent was to explore relationships between religion and fertility preferences that may not have been captured in the initial multivariate survey data analyses. Following intensive fieldwork, the author revised theories about religion's influence, coded new measures from the existing survey data, and added these to survey models to improve statistical fit. This paper discusses the author's research methods, data analyses, and resulting insights for subsequent research, including suggestions for other applications of systematic analyses of anomalous cases using survey and ethnographic methods in tandem.     

Missing Structure: A Critical Content Analysis of the Olweus Bullying Prevention Program

School bullying is dominantly understood as an individualistic phenomenon, overlooking the sociostructural environment that creates and maintains it. A critical content analysis reveals that the Olweus Bullying Prevention Program is similarly lacking. The programme labels students as bullies and victims, ascribes these groups’ particular character traits, sidelines issues of bias and at times reproduces norms that foster bullying. The programme hyperfocuses on real‐time manifestations of bullying incidents, attempting to end bullying through universal monitoring of students and student compliance with anti‐bullying rules. It is argued that efforts to reduce school bullying should address both its individualistic and structural determinants.     

Toward Best Practices in Analyzing Datasets with Missing Data: Comparisons and Recommendations

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.     

Markets,Institutions and Technology: Missing Links in Livelihoods Analysis

The benefits of livelihoods thinking and approaches are widely recognised. This article focuses on an important gap in much of the conceptualization and application of ‘livelihood approaches’— a lack of emphasis on markets and their roles in livelihood development and poverty reduction. The omission is important, as it can lead to failure to identify and act on a wider range of market, institutional and technological opportunities and constraints. An alternative conceptualisation is proposed, with markets as one particular set of institutional mechanisms for co‐ordination and exchange in an economy. It is argued that more explicit attention to interactions between institutions, technology and assets in livelihood analysis may be valuable in conceptualising and managing programmes for livelihood development and poverty reduction.     

Algebraic Representations of Beliefs and Attitudes Ii: Microbelief Models For Dichotomous Belief Data

It may often be the case that the beliefs about which survey researchers query respondents are composed of discrete components, such that holding all of the components isnecessary to give a yes response. Simple logical relations, which some researchers have proposed may structure belief data, may obtain between these components, and not between the beliefs that are actually measured. This paper demonstrates that an algebraic inversion of a data matrix, first used in test theory by Haertel and Wiley (1993), can be seen as a unique and interpretable decomposition that can recover information regarding the compositional formulas of the measured beliefs as well as the logical relations between the unobserved components. The inversion is illustrated with a set of data from the GSS. Finally, the conditions under which related techniques are then helpful or not helpful for analyzing survey data are discussed.     

Log-Multiplicative Association Models As Latent Variable Models For Nominal and/or Ordinal Data

Associations between multiple discrete measures are often due to collapsing over other variables. When the variables collapsed over are unobserved and continuous, log-multiplicative association models, including log-linear models with linear-by-linear interactions for ordinal categorical data and extensions of Goodman's (1979, 1985) RC(M) association model for multiple nominal and/or ordinal categorical variables, can be used to study the relationship between the observed discrete variables and the unobserved continuous ones, and to study the unobserved variables. The derivation and use of log-multiplicative association models as latent variable models for discrete variables are presented in this paper. The models are based on graphical models for discrete and continuous variables where the variables follow a conditional Gaussian distribution. The models have many desirable properties, including having schematic or graphical representations of the system of observed and unobserved variables, the log-multiplicative models can be read from the graphs, and estimates of the means, variances, and covariances of the latent variables given values on the observed variables are a function of the log-multiplicative model parameters. To illustrate some of the advantageous aspects of these models, two examples are presented. In one example, responses to items from the General Social Survey (Davis and Smith 1996) are modeled, and in the other example, panel data from two groups (Coleman 1964) are analyzed.     

Some Log-Linear and Log-Nonlinear Models for Ordinal Scales With Midpoints, With An Application to Public Opinion Data

Ordinal response scales with a middle category are widely used in public opinion studies, psychology, medicine, computed tomography and other fields. The usual models in the statistical literature for ordinal response variables treat the case where the scale has a natural middle category no differently from the case where the scale does not have a middle category. This paper proposes new models for the analysis of ordinal response scales with middle categories, applying these to data collected in 1993-1994 on American opinion toward the balance between environmental quality and economic prosperity. Some of the models should also be useful when the scale does not have a natural middle category. The models are easily used to address issues of concern in empirical work—for example, stochastic ordering among covariate classes and asymmetry about the middle category. Log-linear models are considered in Section 2. The relationship between the normal distribution and a quadratic log-linear model with known scores, discussed in this section, is the basis for Section 3, which considers a log-nonlinear model with unknown scores estimated from the data. Section 4 shows how generalized log-linear and generalized log-nonlinear models can be used to simultaneously study whether the response is below, at, or above the midpoint, and the conditional distribution of responses above (below) the midpoint. These models are also useful when the response scale is viewed as nested and/or the response process is sequential.     

Farm Structure and Economic Well-Being: A Look at Three Methodological Sins

Abstract Sociologists have increasingly turned to secondary data sources to study social and economic change. As our experience in this area increases, so should our methodological rigor. Three common methodological sins—model mis-specification, inattention to regional influences, and fuzzy operationalizations—are illustrated through a critique of Barnes and Blevins' (1992) study of farm structure and economic well-being in nonmetropolitan areas. This paper argues, and demonstrates empirically, that many of Barnes and Blevins' conclusions are suspect because they did not include ethnic and regional variables in their regression studies.     

Nonresponse in the American Time Use Survey: Who Is Missing from the Data and How Much Does It Matter?

This article examines nonresponse in a large government survey,the American Time Use Survey (ATUS), which interviews personsin households previously interviewed in the Current PopulationSurvey. The response rate for the ATUS has been below 60 percentfor the first two years of its existence, raising questionsabout whether the results can be generalized to the target population.The article begins with an analysis of the types of nonresponseencountered in the ATUS. Noncontact accounts for roughly 60percent of ATUS nonresponse, with refusals accounting for roughly40 percent. We find little support for the hypothesis that busypeople are less likely to respond to the ATUS but find considerablesupport for the hypothesis that people who are weakly integratedinto their communities are less likely to respond, mostly becausethey are less likely to be contacted. When we compare aggregateestimates of time use calculated using the ATUS base weightswithout any adjustment for nonresponse, estimates calculatedusing the ATUS final weights with a nonresponse adjustment,and estimates calculated using weights that incorporate ourown nonresponse adjustment based on a propensity model, we findsome modest differences, but the three sets of estimates arebroadly similar. The article ends with suggestions for furtherresearch and analysis.     

Divorce and Family Structure in Norway: Associations With Adolescent Mental Health

The aim of this study was to examine the association between family structure and adolecent mental health, after the considerable increase in divorced parents choosing joint physical custody (JPC) in Norway. Data stem from the youth@hordaland study, a population-based survey conducted in Norway in 2012. A total of 7,707 adolescents (47% male) 16 to 19 years old were included in this study. The adolescents were classified into 6 family structures. Mental health was measured using the Strengths and Difficulties Questionnaire (SDQ). No significant differences between nondivorced families (reference) and JPC were observed. Adolescents from single-parent and stepfather families scored significantly higher on all 3 SDQ scales, and adolescents from stepmother families scored significantly higher on the SDQ total and SDQ externalizing scales. In conclusion, the results of this study indicated that adolescents living in JPC did not have more adjustment problems compared to their peers living in nondivorced families.     

For Refugees,the Road to Employment in the United States Is Paved With Workable Uncertainties and Controversies

Drawing on data—including survey responses, interviews, documents, and participant observation—collected during a 26‐month ethnography of refugees in a northeastern U.S. city, I examine how recently arrived refugees create and access new employment opportunities. I utilize actor‐network theory (ANT) to examine refugees' linkages as emerging, temporal, and fluid. I empirically trace the drawing together of, and interaction among, individual refugees, formal organizations, new cultural ideas, and a myriad of material objects. I examine the connections between the uncertainties about actors, action, and agency that point to the need to understand society as sociomaterial networks. I analyze the controversies that are deployed in an emerging assemblage as the refugees entered the paid workforce in the United States. I am guided by a broad question: How are meaning, knowledge, and facts that come to make up a network actually made, maintained, remade, and, sometimes, undone? I demonstrate that putting assemblage to work offers insights into the ways in which heterogeneous elements come together in often unanticipated ways to create stable, even if temporary, employment networks for refugees in the United States.     

Missing Data in Sociological Research: An Overview of Recent Trends and an Illustration for Controversial Questions, Active Nonrespondents and Targeted Samples

In an age of telemarketers, spam emails, and pop-up advertisements, sociologists are finding it increasingly difficult to achieve high response rates for their surveys. Compounding these issues, the current political and social climate has decreased many survey respondents’ likelihood of responding to controversial questions, which are often at the heart of much research in the discipline. Here we discuss such implications for survey research in sociology using: a content analysis of the prevalence of missing data and survey research methods in the most cited articles in top sociology journals, a case study highlighting the extraction of meaningful information through an example of potential mechanisms driving the non-random missing data patterns in the Religion Among Academic Scientists dataset, and qualitative responses from non-responders in this same case. Implications are likely to increase in importance given the ubiquitous nature of survey research, missing data, and privacy concerns in sociological research.     

Quantitative Models and Methods for World-System Analysis*

Many quantitative analysts who have turned their attention to the world-system have developed models of the structural and relational components of that system. These efforts have come under fire, however, from those who claim that statistical techniques, particularly regression, are not suited to analyses of the world-system. This paper presents some of the major criticisms of quantitative efforts, a response to each, and a series of general regression models with applicability to world-system analyses. Discussion of the means by which data may be selected for the proposed analytical models is also offered.     

EAP Benefit and Cost Structure Analysis:

This presentation is a review of basic estimation methods geared for assessment of service benefit and cost structure of employee assistance programs (EAPs). The methodologies specify how various benefit and cost items can be organized and measured in dollar figures in order to translate effects of the EAP and operating cost distribution patterns. Based on the calculations, EAPs may assess cost saving patterns and avoid becoming a financial liability to the company. The estimation of benefit/cost ratios can be utilized for establishing program accountability and planning purposes. The methodologies presented in this chapter are at an introductory level. They are geared for EAP personnel and social work students interested in EAPs who have minimal familiarity with cost and benefit structure analysis.