Mutual assent or unilateral nomination? A performance comparison of intersection and union rules for integrating self-reports of social relationships

Data collection designs for social network studies frequently involve asking both parties to a potential relationship to report on the presence of absence of that relationship, resulting in two measurements per potential tie. When inferring the underlying network, is it better to estimate the tie as present only when both parties report it as present or do so when either reports it? Employing several data sets in which network structure can be well-determined from large numbers of informant reports, we examine the performance of these two simple rules. Our analysis shows better results for mutual assent across all data sets examined. A theoretical analysis of estimator performance shows that the best rule depends on both underlying error rates and the sparsity of the underlying network, with sparsity driving the superiority of mutual assent in typical social network settings.     

Dynamic Discrete-time Duration Models: Estimation via Markov Chain Monte Carlo

Discrete-time or grouped duration data, with one or multiple types of terminating events, are often observed in social sciences or economics. In this paper we suggest and discuss dynamic models for flexible Bayesian nonparametric analysis of such data. These models allow simultaneous incorporation and estimation of baseline hazards and time-varying covariate effects, without imposing particular parametric forms. Methods for exploring the possibility of time-varying effects, as for example the impact of nationality or unemployment insurance benefits on the probability of reemployment, have recently gained increasing interest. Our modeling and estimation approach is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques. A detailed analysis of unemployment duration data, with full-time job, part-time job and other causes as terminating events, illustrates our methods and shows how they can be used to obtain refined results and interpretations.     

Social Contagion and General Diffusion Models of Adolescent Religious Transitions: A Tutorial,and EMOSA Applications

Epidemic Models of the Onset of Social Activities (EMOSA) describe behaviors that spread through social networks. Two social influence methods are represented, social contagion (one-to-one spread) and general diffusion (spread through cultural channels). Past models explain problem behaviors—smoking, drinking, sexuality, and delinquency. We provide review, and a tutorial (including examples). Following, we present new EMOSA models explaining changes in adolescent and young adult religious participation. We fit the model to 10 years of data from the 1997 U.S. National Longitudinal Survey of Youth. Innovations include a three-stage bi-directional model, Bayesian Markov Chain Monte Carlo (MCMC) estimation, graphical innovations, and empirical validation. General diffusion dominated rapid reduction in church attendance during adolescence; both diffusion and social contagion explained church attendance stability in early adulthood.     

When Parents and Adolescents Disagree About Disagreeing: Observed Parent–Adolescent Communication Predicts Informant Discrepancies About Conflict

Adolescence is accompanied by increased stress in the parent–adolescent relationship, which frequently results in conflict. Researchers often rely on self‐reports to measure conflict, but these reports are frequently discrepant from one another. In two studies, we examined the extent to which communication observed during parent–adolescent discussions of conflict were associated with discrepancies in reports about conflict. We also examined links between informant depressive symptoms and discrepancies. Across studies, observed parent–adolescent conflict behaviors consistently predicted absolute discrepancies in reports of conflict. Informant depressive symptoms sometimes predicted directional discrepancies in reports. Results suggest that informant discrepancies about conflict may stem, in part, from a lack of open communication in the relationship.     

Informant accuracy in social network data III: A comparison of triadic structure in behavioral and cognitive data

This paper provides a comparison of the triadic-level structure inherent in behavioral and cognitive social network data taken on the same group, using a variety of groups whose communication could easily be monitored.It is found that many types of structure occur significantly more or less than chance in both behavioral and cognitive data, and providing that these are treated in similar ways, there is good agreement between the two structures. However, there are several ways to treat behavioral data, and these produce at least two essentially different structures.If cognitive and behavioral triads are compared, triad by triad, then there is virtually no agreement between them (even though they may both display the same structure on an overall triad census).Finally, as a demonstration of the dangers of relying solely on cognitive data, an unlikely null hypothesis is proposed. This asserts — for demonstration purposes — that, under many circumstances, behavioral structure never alters. Change in structure over time apparently occurs because of informant error in the reporting of the cognitive data. A pseudo-transition matrix, giving the probability that a triad is reported as one type when data are first taken, and a different type at a later date, is calculated. This compares reasonably with a genuine transition matrix evaluated for longitudinal cognitive data. It is believed that no data currently exist which can disprove this hypothesis, unlikely though it is. Much more accurate data are therefore necessary if any reliable theory of social structure is to be produced.     

Social work as gendered issue from a generational point of view

Social work is a gendered activity in terms both of its workforce and of the students who enter this profession. In this paper we analyse the underlying dynamics of this phenomenon in the Italian context, according to the characteristics and points of view of different generations of social workers. Data from a 2008 survey of 1000 interviews and 50 in-depth informant interviews are considered. Two multinomial linear models have been included in order to analyse the perceptions of younger and older social workers with regard to social work as ‘female work’ or ‘male work’. The results show that the perception of social work as a matter of gender is stronger among younger social workers, and that the younger generations appear to be less oriented towards equal gender roles. Longer work experience contributes to explaining different gender models. The chronological age of social workers, however, seems to be a less important variable in explaining the differences between generations.     

Analysing networks of networks

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.     

Discrepancies About Adolescent Relationships as a Function of Informant Attachment and Depressive Symptoms

Developmental scientists studying interpersonal relationships often find that informants disagree in their reports, and two theoretical perspectives suggest that these discrepancies may be predictable. In two studies of adolescents' interpersonal relationships, we examined two factors that may predict the absolute magnitude and the direction of the discrepancies in reports about several types of relationships. Specifically, we examined informants' depressive symptoms and attachment as predictors of absolute and directional discrepancies in reports about (a) adolescents' relationships with peers, (b) parent–adolescent relationships, and (c) adolescents' friendships. Findings revealed that informant depressive symptoms sometimes were associated with discrepancies. In contrast, informant attachment more consistently accounted for absolute and directional discrepancies.     

Timber,Minerals, and Social Change: An Exploratory Test of Two Resource-Dependent Communities1

Abstract There are few empirical tests of the relationship between local resource production and community social systems. This paper reports the results of research on two communities in northern Idaho: one timber-dependent, the other mining-dependent. Data were collected for 13 indicators of resource production and 15 indicators of social change for periods up to 65 years. Regression analysis was used to test if community social change is associated with the production level of local resource systems. The final models support the hypothesis in both communities. The form, lag condition, and strength of the relationship is complex and may vary with the dependent indicator. The original causal image—the rural community with a social order that directly responds to changes in local resource production—is blurred. Suggestions are made for future research.     

MULTIPLE IMPUTATION OF INCOMPLETE CATEGORICAL DATA USING LATENT CLASS ANALYSIS

We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To obtain imputations reflecting uncertainty about the unknown model parameters, we use a nonparametric bootstrap procedure as an alternative to the more common full Bayesian approach. The proposed multiple imputation method, which is implemented in Latent GOLD software for latent class analysis, is illustrated with two examples. In a simulated data example, we compare the new method to well-established methods such as maximum likelihood estimation with incomplete data and multiple imputation using a saturated log-linear model. This example shows that the proposed method yields unbiased parameter estimates and standard errors. The second example concerns an application using a typical social sciences data set. It contains 79 variables that are all included in the imputation model. The proposed method is especially useful for such large data sets because standard methods for dealing with missing data in categorical variables break down when the number of variables is so large.     

Strengths And Struggles: Overseas Qualified Social Workers' Experiences In Aotearoa New Zealand

Abstract

A growing number of professionals practise in a country other than the one where they obtained their professional qualification. A mixed-methods study explored the experiences of one group of such professionals, overseas-qualified social workers practicing in New Zealand. This article reports findings from four key informant group interviews and an online survey (N = 294), including this population's satisfaction with their employment, their introduction to local practice, and experiences of mistreatment and discrimination. The study highlights migrant social workers' desire for better access to information and induction opportunities to orient them to the local practice environment and notes their experience of discrimination in the workplace. Such findings require a thoughtful response from practitioners and professional bodies and is worthy of further research and discussion. We argue that migrant social workers in New Zealand require orientation activities tailored to meet their needs and a more welcoming system of recognition of the skills and experience they bring.     

Coming Closer in Adolescence: Convergence in Mother,Father, and Adolescent Reports of Parenting

Parent–child relationships change during adolescence. Furthermore, parents and adolescents perceive parenting differently. We examined the changes in perceptions of parental practices in fathers, mothers, and adolescents during adolescence. Furthermore, we investigated if fathers', mothers', and adolescents' perceptions converge during adolescence. Following 497 families across six waves (ages 13–18), we investigated the development of parental support and behavioral control using mother and father self‐reports, and adolescent reports for mothers and fathers. We found curvilinear decrease for support and control. Parent–adolescent convergence emerged over the 6 years: those with higher intercepts had a steeper decrease, whereas correlations among parent and adolescent reports increased. This multi‐informant study sheds light on the development of parent–adolescent convergence on perceptions of parenting.     

Measurement quality issues in dyadic models of relationships

Interpersonal relationships are an important and integral part of numerous social science research agendas. Analytical tools have been created in the last 10 years that model dyadic interactions. In particular, this article focuses on the dyadic models of Fienberg and Wasserman [Fienberg, S.E., Wasserman, S., 1981. Categorical data analysis of single sociometric relations. In: Leinhardt, S. (Ed.), Sociological Methodology. Jossey-Bass, San Francisco.], Holland and Leinhardt [Holland, P.W., Leinhardt, S., 1981. An exponential family of probability densities for directed graphs. Journal of the American Statistical Association 76 (1981) 33–51.], Iacobucci and Wasserman [Iacobucci, D., Wasserman, S., 1988. A general framework for the statistical analysis of sequential dyadic interaction data. Psychological Bulletin 103 (1988) 379–390.] and Wasserman and Iacobucci [Wasserman, S., Iacobucci, D., 1986. Statistical analysis of discrete relational data. British Journal of Mathematical and Statistical Psychology 39 (1986) 41–64.]. However, measurement issues like reliability and validity, as discussed by Allen and Yen [Allen, M.J., Yen, W.M., 1979. Introduction to Measurement Theory. Brooks/Cole, Monterey, CA, 1979.], Nunnally [Nunnally, J., 1978. Psychometric Theory, 2nd edn. McGraw-Hill, New York, NY, 1978.] and Uebersax [Uebersax, J.S., 1988. Validity inferences from interobserver agreement. Psychological Bulletin 104 (1988) 405–416.], have not been considered in conjunction with these models, and little is known about the empirical performance of the dyadic models under sub-optimal measurement quality conditions. We offer two essential approaches to ascertaining the level of measurement error in the observed indicators of social ties and relationships. The first approach combines latent class and social network models in one integrated framework and allows for the simultaneous study of measurement and dyadic structural issues. The second approach is an alternative that may be more useful to social science researchers, both because the method is more accessible and because researchers could apply the techniques to data they have already partially analyzed. This approach is a two-staged procedure whereby in the first stage, a probability model based on latent class analysis is estimated which provides an indication of the measurement quality in the data. In the second stage, traditional social network models are estimated. To investigate the implications of different levels of measurement error for interpreting the nature of the network ties and the dyadic parametric performance, we also designed a Monte Carlo experiment. Measurement error is simulated as the likelihood of a binary relational choice (for simplicity) being inaccurately classified, where incorrect diagnoses can result from poor interitem agreement (i.e., unreliability) or poor interrater agreement. The simulation can be used by researchers in combination with the two-stage approach. The results of the simulation provide guidelines for situations when social network models can withstand a reasonable degree of sub-optimal measurement quality and highlight adverse conditions which can significantly affect the performance of the modeling approach. Further, the simulation shows that sample size assists in reducing the chances of making Type II errors, but it does not compensate for biases in parameter estimates in the presence of increasing error. Finally, the measurement and dyadic analytical methods are applied to a real dataset describing interorganizational relational activity using multiple raters. Recommendations are offered to guide the researcher in making decisions about research design when using dyadic models.     

An Introduction and Illustration of Bayesian Modeling in Couple and Family Therapy Research

Bayesian modeling is becoming increasing popular as a method for data analyses in the social sciences and can move couple, marriage, and family therapy (C/MFT) research forward. Bayesian modeling helps researchers better understand the uncertainty of findings and incorporate previous research into analyses. Other benefits of Bayesian modeling are the straightforward interpretation of findings, high-quality inferences even with small samples (in combination with an informative prior), and the ability to work with complex data structures (observations nested in relationships and time points) which are common in C/MFT research. This article introduces the benefits of Bayesian modeling and provides an example of an Actor–Partner Interdependence Model using R. Information on how to conduct the same analyses using Stata and MPlus is provided in the Supplemental Information.     

BAYESIAN INFERENCE FOR HIERARCHICAL AGE-PERIOD-COHORT MODELS OF REPEATED CROSS-SECTION SURVEY DATA

This study applies methods of Bayesian statistical inference to hierarchical APC models for the age-period-cohort analysis of repeated cross-section survey data. It examines the impacts of small sample sizes of birth cohorts and time periods and unbalanced data on statistical inferences based on the usual restricted maximum likelihood–empirical Bayes (REML-EB) estimators through Monte Carlo simulations. A full Bayesian analysis using Gibbs sampling and MCMC estimation is developed to assess the robustness of REML-EB inferences when this extra uncertainty is taken into account and the numbers of higher-level units are small. For a substantive illustration, it applies cross-classified random effects models to vocabulary test data from the General Social Survey (1974 to 2000). It is concluded that the decline in verbal ability for birth cohorts born after 1950 was correlated with the levels of newspaper reading and television watching. Avenues for future research on mixed APC models are discussed.     

DIRECT TESTS OF INDIVIDUAL PREFERENCES FOR EFFICIENCY AND EQUITY

Departures from “economic man” behavior in many games in which fairness is a salient characteristic are now well documented in the experimental economics literature. These data have inspired the development of models of social preferences that assume agents have preferences for equity and efficiency as well as their own material payoffs. Empirical failure of the economic man model comes from experiments that provide direct tests of its distinguishing characteristic: indifference to the payoffs of others. This paper reports an experiment that subjects popular social preferences models to the same type of empirical challenge. We report direct tests of the distinguishing characteristics of these models: preference for allocations that have higher efficiency and greater equity. (JEL A12, A13, B49, C70, C91, D63)     

Prevalence of Elder Mistreatment as Reported by Social Workers in a Probability Sample of Adult Day Health Care Clients

ABSTRACT

There are growing numbers of Adult Day Health Care (ADHC) programs providing needed health services to community-dwelling elderly. Therefore, ADHC staff are in an opportune position to identify and to intervene in suspected elder mistreatment (EM) cases. In this paper, prevalence estimates of EM are reported for a probability sample of ADHC clients in New York State, using data provided during a social worker informant interview. The abuse “signs and symptoms” items in the social worker informant interview schedule were divided into two categories: (1) physical indicators and (2) client's behavioral indicators. Physical indicators included unexplained bruises and welts, unexplained burns, unexplained lacerations or abrasions, human bite marks, and frequent injuries that are “accidental” or “unexplained.” Client's behavioral indicators included apprehension, being frightened, and afraid to go home. EM prevalence for all 8 items was 12.3 percent. When “apprehensive” was excluded, the EM prevalence was 3.6 percent in this sample. These findings suggest that ADHC provides a point of contact for the assessment and intervention of EM that might otherwise be overlooked among elders who are often isolated or homebound.     

Working with the Experts: collaborative research with people with an intellectual disability

This paper reports on part of a wider study concerned with the collaborative efforts of an inquirer and six people with an intellectual disability, to develop a grounded theory explaining the processes by which these informants manage the relationships within their personal communities. The study was conducted through a series of in-depth interviews with each informant; a process characterised by information sharing, tentative theory development and elaboration, and informants' checking the accuracy of the emerging theory. This inductive approach allowed not only an insight into the lives of each informant, but recognised the informants' expertise in matters concerning their own lives and thus facilitated the emergence of theoretical elements of relevance to the informants themselves. The focus of the paper is on the collaborative or partnership approach adopted. The outcomes of the research partnership are detailed and implications drawn for the role of research in the lifestyles of people with an intellectual disability.     

Imputation of attributes in networked data using Bayesian autocorrelation regression models

Misspecification in network autocorrelation models poses a challenge for parameter estimation, which is amplified by missing data. Model misspecification has been a focus of recent work in the statistics literature and new robust procedures have been developed, in particular cutting feedback. This paper shows how this helps in a misspecified network autocorrelation model. Where model misspecification is mild and the traits are fully observed, Bayesian imputation is routine. In settings with high missingness, Bayesian inference can fail, but a closely related cut model is robust. We illustrate this on a data set of graduate students using a Facebook-like messaging app.