Explaining interviewer effects and respondent behavior: Theoretical models and empirical analysis

Reinecke J, Schmidt P. Explaining interviewer effects and respondent behavior: Theoretical models and empirical analysis. Quality and Quantity. 1993;27(3):219-247.The behavior of respondents in interview situations has been dealt on the one hand with respect to many empirical studies and on the other hand in connection with different theoretical approaches (Hyman 1954; Cannell & Kahn 1968). In this paper the most relevant theoretical explanations are discussed and systematized from the point of view of the theory of reasoned action (Ajzen & Fishbein 1980). Whereas this theory program has been used in many substantive fields, it has rarely been applied to the problem of interviewer effects and response sets. In this approach one assumes that the actors in interview situations decide according to cost-benefit calculations. The theory of reasoned action is viewed as an operationalized theory, discussed in more detail and formalized via structural equation models. These models are empirically tested with the data of a survey specifically designed to perform such a method study. The reported contact rates of German with foreigners is the dependent variable under study. First, a model without interviewer variables is tested to explain respondent behavior in terms of norms, attitudes and some other determinants. Then the status and the age of interviewers are introduced as situational determinants of the respondents' behavior. For subgroup analyses the respondents are divided into three groups varying in the amount of the need for social approval. The models are tested according to two subgroups (low and high need for social approval) with the technique of multiple group comparison in LISREL (Jöreskog/Sörbom 1988). All models and results are interpreted in terms of the theory of reasoned action. At the end some conclusions for modelling interviewer effects and respondent behavior are discussed

Latent class analysis with panel data: developments and applications

'In der vorliegenden Arbeit wird das statistische Modell der Analyse latenter Klassen nach der Parametrisierung von Lazardsfeld vorgestellt. Den Schwerpunkt bilden Entwicklungen und Anwendungen der Analyse latenter Klassen auf Paneldaten. Das latente Markov Modell erlaubt sowohl Restriktionen über zeitbezogene Gleichsetzungen von konditionalen Wahrscheinlichkeiten als auch Restriktionen der Übergangswahrscheinlichkeiten zwischen den latenten Variablen. Die allgemeinste Variante ist das latente mixed Markov Modell. Dieses Modell verfügt über zusätzliche Spezifikationsmöglichkeiten der unbeobachteten Heterogenität mit Markov Ketten. Empirische Beispiele, durchgeführt mit PANMARK, verdeutlichen die jeweiligen Modellierungstechniken.' (Autorenreferat)'The present paper discusses the statistical model of the latent class analysis according to the parametrization of Lazarsfeld. Developments and applications of latent class analysis with panel data are the main topic of this paper. The latent Markov model allows time-specific restrictions of the conditional probabilities as well as restrictions of the transition probabilities between the latent variables. The most general model, the latent mixed Markov model, has additional opportunities to specify unobserved heterogeneity via different Markov chains. Empirical examples, calculated with PANMARK elucidate the relevant modeling techniques.' (author's abstract)

Interaktionseffekte in Strukturgleichungsmodellen mit der Theorie des geplanten Verhaltens: multiple Gruppenvergleiche und Produktterme mit latenten Variablen

'Dieser Beitrag thematisiert Modellierungsmöglichkeiten von Interaktionseffekten in Strukturgleichungsmodellen. Wert x Erwartungsprodukte zu den Konstrukten Einstellung, subjektive Norm und wahrgenommene Verhaltenskontrolle, formuliert in der Theorie des geplanten Verhaltens, werden hierzu herangezogen. Anhand einer repräsentativen Stichprobe von Jugendlichen und jungen Erwachsenen kann mit multiplen Gruppenvergleichen und latenten Produktmodellen gezeigt werden, daß für das Wert x Erwartungsprodukt der wahrgenommenen Verhaltenskontrolle ein signifikanter Interaktionseffekt vorliegt. Der Stellenwert unterschiedlicher Schätzverfahren (ML, GLS und WLS) wird in bezug auf die latenten Produktmodelle diskutiert.' (Autorenreferat)'The article discusses strategies of modeling interaction effects in structural equations. Expectancy x value products of the constructs attitude, subjective norm and perceived behavioral control of the Theory of Planned Behavior are considered. Multiple group comparisons as well as latent product models with data from a representative sample of adolescents and young adults show a significant interaction effect of the expectancy x value product of perceived behavioral control. The usefulness of different estimation procedures (ML, GLS and WLS) is discussed in relation to the latent product models.' (author's abstract)

Detection of unobserved heterogeneity with growth mixture models

Latent growth curve models as structural equation models are extensively discussedin various research fields (Duncan et al., 2006). Recent methodological and statisticalextension are focused on the consideration of unobserved heterogeneity in empiricaldata. Muth´en extended the classical structural equation approach by mixture components,i. e. categorical latent classes (Muth´en 2002, 2004, 2007).The paper will discuss applications of growth mixture models with data from oneof the first panel studies in Germany which explore deviant and delinquent behavior ofadolescents (Reinecke, 2006a, 2006b). Observed as well as unobserved heterogeneitywill be considered with growth mixture models using the program Mplus (Muth´en& Muth´en, 2006). Special attention is given to the distribution of the substantivedependent variables as a count measures (Poisson distribution, zero-inflated Poissondistribution, cf. Nagin, 1999). Different model specifications with respect to substantivequestions will also be emphasized.Keywords: Panel data, growth mixture models, heterogeneity, Poisson distribution.Los modelos latentes de curvas de crecimiento, como modelos de escuaciones estructurales,son ampliamente discutidos en varios campos de investigaci´on (Duncanet al., (2006)). Extensiones metodol´ogicas y estad´?sticas recientes se enfocan en laconsideraci´on de heterogeneidad no observada en datos emp´?ricos. Muth´en extendi´oel enfoque cl´asico de ecuaciones estructurales por componentes de mezcla, es decirclases latentes categ´oricas (Muth´en 2002, 2004, 2007).El art´?culo discute aplicaciones de modelos de crecimiento de mezcla con datosde uno de los primeros estudios de panel en Alemania, que explora comportamiento desviado y delinquivo de adolescentes (Reinecke, 2006a, 2006b). La heterogeneidadobservada y no observada ser´a considerada con modelos de crecimiento de mezclausando el programa Mplus (Muth´en & Muth´en, 2006). Se dar´a especial atenci´ona la distribuci´on de las variables sustantivas dependientes como medidas de conteo(distribuci´on de Poisson, distribuci´on cero-inflada de Poisson, cf. Nagin, 1999). Sedar´a ´enfasis tambi´en a diferentes especificaciones de modelos con respecto a cuestionesimportantes.Palabras clave: Datos de panel, modelos de mezclas de crecimiento, heterogeneidad,distribuci´on de Poisson

Multiple imputation of incomplete ordinary and overdispersed count data

Kleinke K, de Jong R, Spiess M, Reinecke J. Multiple imputation of incomplete ordinary and overdispersed count data.; 2011.Throughout the last couple of years multiple imputation (MI) has become a popular and widely accepted method to address the missing data problem. However, MI solutions for incomplete count data are still not available in most statistical packages. We present count data imputation add-ons for the popular mice software in R (van Buuren & Groothuis-Oudshoorn, 2011). Our add-on functions allow to create multiple imputations of incomplete ordinary and overdispersed count data following the chained equations approach of creating multiple imputations (cf. Raghunathan, Lepkowski, van Hoewyk, & Solenberger, 2001; van Buuren & Groothuis-Oudshoorn, 2011). We furthermore present evaluations of these solutions regarding their ability to produce unbiased parameter estimates and standard errors as well as their ability to cope with missing not at random mechanisms

Ontology for Cultural Variations in Interpersonal Communication: Building on Theoretical Models and Crowdsourced Knowledge

The domain of cultural variations in interpersonal communication is becoming increasingly important in various areas, including human-human interaction (e.g. business settings) and human-computer interaction (e.g. during simulations, or with social robots). User generated content (UGC) in social media can provide an invaluable source of culturally diverse viewpoints for supporting the understanding of cultural variations. However, discovering and organizing UGC is notoriously challenging and laborious for humans, especially in ill-defined domains such as culture. This calls for computational approaches to automate the UGC sensemaking process by using tagging, linking and exploring. Semantic technologies allow automated structuring and qualitative analysis of UGC, but are dependent on the availability of an ontology representing the main concepts in a specific domain. For the domain of cultural variations in interpersonal communication, no ontological model exists. This paper presents the first such ontological model, called AMOn+, which defines cultural variations and enables tagging culture-related mentions in textual content. AMOn+ is designed based on a novel interdisciplinary approach that combines theoretical models of culture with crowdsourced knowledge (DBpedia). An evaluation of AMOn+ demonstrated its fitness-for-purpose regarding domain coverage for annotating culture-related concepts mentioned in text corpora. This ontology can underpin computational models for making sense of UGC

Einführung

Reinecke J, Tarnai C. Einführung. In: Reinecke J, ed. Klassifikationsanalysen in Theorie und Praxis. Münster, New York: Waxmann; 2008: 13-18

Beobachtete und unbeobachtete Heterogenität im Delinquenzverlauf

Reinecke J. Beobachtete und unbeobachtete Heterogenität im Delinquenzverlauf. In: Boers K, Reinecke J, eds. Delinquenz im Jugendalter: Erkenntnisse aus einer Münsteraner Längsschnittstudie. Kriminologie und Kriminalsoziologie. Vol 3. Münster, New York: Waxmann; 2007: 129-145