Adolescent-adult interaction and peer-group involvement.

The principle purpose of this paper has been to assess the importance of the quality of adolescent-adult interaction in explaining differentials in adolescent peer-group involvement. The analysis indicated that quality of adolescent-adult interaction is inversely related to peer-group involvement and that the relationship is statistically significant beyond the .001 level. This holds true for each dimension of peer-group involvement included in the analysis. Thus, the analysis supports the hypothesis.     

Development of a microcomputer system for verbal interaction analysis

Verbal interaction analysis has been demonstrated to be a valuable procedure for research on temporal, noncontent variables in dyadic interviews. Temporal variables include durations of utterance, reaction time latency, initiative time latency, and overlap, which are recorded for both parties in the interview. Instruments for the measurement of these variables have been both complex and expensive. An Apple II microcomputer is programmed to record the four temporal variables in verbal interaction analysis, providing a portable, less expensive, and convenient instrument.     

A framework for the interpretation of first-order interaction in logit modeling.

Several suggestions have been tendered for interpreting first-order interaction in log-linear analysis. Occasionally these methods result either in a loss of information or in results that are difficult to grasp on an intuitive level. It is argued that interpreting effect parameters in terms of odds ratios provides an elegant and intuitively appealing conceptual framework that has great generality across models. The interaction term then resembles a cross-product term in linear regression. In both forms of analysis, the partial effect of a given predictor on the response is composed of a constant and a correction that is a function of the other predictor involved in the interaction. This framework is especially appealing for models in which the logit is based on a bifurcation of the dependent variable. Although odds ratios are still useful for summarizing effects on polytomous dependent variables, greater caution must be exercised to avoid misleading interpretations.     

Conversation analysis,cyberpsychology and online interaction

In this paper, I explore how conversation analysis (CA) can be used as a method for analysing online interaction. As the number and quantity of online communication platforms have proliferated, there has been a growing interest in social psychology about the impact and effectiveness of online, text‐based communication. A number of theories have been used and developed to explain how online communication might impact upon relationships and effective communication. However, this paper argues that in order to explore the differences between online and offline interaction an analysis of online behaviour is needed. CA allows for an in‐depth, sequential and discursive analysis of real‐life online interactions. It can explore the ways in which the affordances of the interactional platforms are oriented to or made relevant in the interaction. The utility of CA is demonstrated through a number of examples, highlighting how this method can be used to broaden our understanding of how online communication works in practise.     

Interaction in ANOVA: misconceptions

Although interaction in analysis of variance has an unequivocal theoretical meaning (and so it appears in the statistic literature), frequent misconceptions are found in empirical research, which, in many cases, lead to wrong conclusions. In this paper, 150 articles are reviewed: in 12.7% of them, no attention is paid to the interaction (either because it is not analysed or because it is analysed but not discussed); in 79.1%, interaction is studied through simple effects analysis; and only in 8.2% of the cases, interaction is correctly discussed. It could be that psychology researchers tend to analyse and interpret the interaction between factors incorrectly because the most widespread statistic packages (with SPSS in the lead) do not allow performing the comparisons needed to analyse a significant interaction in factorial designs with randomized groups. In order to contribute to eradicating this problem, we herein show how to design some of the linear comparisons that allow isolating the interaction effect, and we explain how to use SPSS to compute these comparisons.     

What Predicts Change in Marital Interaction Over Time? A Study of Alternative Models

This is a report on what predicts the deterioration of affective marital interaction over a 4-year period. Four models were compared for their ability to predict Time-2 dysfunctional marital interaction (a set of reliable predictors of marital dissolution). These four models were: (1) baseline physiology at Time-1; (2) interaction physiology at Time-1; (3) a balance model based on the ratio of positivity to negativity at Time-1; and, (4) cognitions about the relationship operationalized from our coding of the Oral History Interview. All four models predicted Time-2 dysfunctional marital interaction. All four models were also able to predict change, operationalized as predicting Time-2 interaction, controlling for Time-1 interaction, that is, using a covariance regression analysis. The most powerful model in predicting change was the balance ratio model.     

LONELINESS,PARASOCIAL INTERACTION,AND LOCAL TELEVISION NEWS VIEWING

A conceptual model was developed predicting parasocial interaction from both a social interaction need due to loneliness and instrumental television news use. Questionnaires were completed by 329 persons. Pearson and partial correlations supported hypotheses linking loneliness with less interpersonal communication and both loneliness and parasocial interaction with more television reliance. Loneliness and parasocial interaction were not correlated. Canonical correlation analysis supported expectations that instrumental news viewing for information was related to more parasocial interaction and perceived news realism; viewing news for exciting entertainment, news affinity, and news viewing levels correlated positively with this pattern. Ritualized news viewing for time consumption was related to more television viewing, but to less news viewing, duration, and affinity. Hierarchical multiple regression analysis identified news affinity, perceived news realism, and information news viewing motives as salient predictors of parasocial interaction with a favorite local television news personality. Implications of results were discussed in light of uses and gratifications research and communication interaction.     

Interactions in processing segmental and suprasegmental features of speech

A speeded classification task was used to determine the nature of the dependency relation that exists during the processing of a segmental distinction in vowel quality and a suprasegmental distinction in pitch (Experiment 1) or loudness (Experiment 2) in a consonantvowel syllable. In both experiments, evidence was found for a mutual, symmetrical dependency between the processes underlying the analysis of the segmental and suprasegmental information in the syllable. This type of interaction pattern contrasts with the pattern of unidirectional dependency previously found for the analysis of a consonantal distinction and a suprasegmental distinction, in which the analysis of the consonantal information was found to be dependent on the analysis of the suprasegmental information (e.g., Wood, 1974). Together with these earlier findings, the present results clearly indicate that the form of the interaction between processes responsible for segmental and suprasegmental analysis is a function of the type of segmental information being analyzed. Future research must determine whether the distinct interaction patterns found thus far for consonants and vowels are due to a difference in phonetic class per se, i.e., consonant vs. vowel, or to a difference in the nature of the acoustic information specifying the consonant and vowel.     

Interaction analysis: meaningless in the face of relevance

Interaction analysis has become a standard tool for process-oriented research. Although now a researcher has a choice of instruments, the majority of these are based on the same concept of communication: the sender-receiver model. Communication is treated as a discrete and encoded process that progresses uniformly in time. We critically discuss these assumptions and the limits they put on communication research. It is asserted that instruments for interaction analysis based on the sender-receiver model restrict themselves to the more superficial levels of interaction and are unable to register the more significant events. Therefore they cannot account for a very essential feature of the communication process, namely the creation of meaning. Alternative options are discussed.     

A framework for the multimodal joint work of turn construction in face-to-face interaction

We propose a framework for understanding the multimodal joint work of turn construction in face-to-face interaction. Using concepts from conversation analysis, nonverbal communication, and gesture studies, in a qualitative analysis of face-to-face interaction, we observe that, collaboratively and in a joint work, participants produce moves, within the current-speaker’s turn, that allow them to deal with possible moves that could compromise the projectable trajectory of the interaction in progress. Working at the micro level of interaction, we propose a framework that will allows a better understanding of how a turn can be collaboratively produced and how other levels of sequence organization can be produced in order to achieve the desired social-agreement outcome.     

Interactions do not only tell us when,but can also tell us how: Testing process hypotheses by interaction

Hypotheses about psychological processes are often tested using traditional mediation analysis. This analysis relies on measurement of a transmitting variable. Conducting this analysis has become almost synonymous with examining process hypotheses. An alternative strategy to mediation analysis (the Testing‐a‐Process‐hypothesis‐by‐an‐Interaction Strategy, TPIS) is illustrated here. TPIS is based on a fully experimental design whereby a hypothesized process is tested by an interaction between the hypothesized cause of an effect and a contextual variable. In the interaction term, the contextual variable allows comparison of the causal effect observed when the process is uninterrupted to the effect observed when the process is interrupted. Thus, TPIS translates a theoretical process hypothesis into a statistical interaction hypothesis that uses a fully experimental design to directly examine the hypothesized process. Copyright © 2010 John Wiley & Sons, Ltd.     

Modeling Temporal Interaction Dynamics in Organizational Settings

Most workplace phenomena take place in dynamic social settings and emerge over time, and scholars have repeatedly called for more research into the temporal dynamics of organizational behavior. One reason for this persistent research gap could be that organizational scholars are not aware of the methodological advances that are available today for modeling temporal interactions and detecting behavioral patterns that emerge over time. To facilitate such awareness, this Methods Corner contribution provides a hands-on tutorial for capturing and quantifying temporal behavioral patterns and for leveraging rich interaction data in organizational settings. We provide an overview of different approaches and methodologies for examining temporal interaction patterns, along with detailed information about the type of data that needs to be gathered in order to apply each method as well as the analytical steps (and available software options) involved in each method. Specifically, we discuss and illustrate lag sequential analysis, pattern analysis, statistical discourse analysis, and visualization methods for identifying temporal patterns in interaction data. We also provide key takeaways for integrating these methods more firmly in the field of organizational research and for moving interaction analytical research forward.     

(Spi)ritual interaction in musical activity for infants in the church space – a qualitative analysis

Abstract

Early mother–infant interaction has been seen as very important for the development of religiosity and spirituality. This case study examines one specific musical activity for infants and mothers from the perspective of human interaction. The studied activity is ‘Babysalmesang’, or baby hymn singing (BHS), within the Evangelical Lutheran Church in Denmark. Meetings are held in church buildings and include singing hymns and other songs, playing in and exploring the church space. The qualitative empirical data on BHS activities were collected through video recording, observing and interviewing. The initial analysis on the data showed that four types of interaction can be found: mother–infant interaction, interaction between the participants, interaction between the participants and instructors and ritual interaction. BHS can be seen as a space for (spi)ritual interaction. In this context, nurturing spirituality combines with religious elements. Together with mother–infant interaction, also other types of interaction might nurture spirituality in early childhood.     

New Procedures for Analyzing Relational Communication

The purpose of this paper is to present a system for describing and indexing patterns of communication at the relational level, primarily in dyadic systems. Relational communication refers to the control aspects of message exchanges that define an interactor's relationships with others. The reciprocal definition of each individual's role at a given moment in the interaction is reflected in the relative control each individual has and/or is given by the other individual. The control-defining aspects of communication, rather than other information such communication may convey, is the focus of the coding system to be described. Further, the analysis deals with sequences of messages rather than individual messages, and attempts to index the control dimensions of those messages according to their similarity or difference. While previous interaction analysis techniques have been predominantly monadic in nature, i.e., single-message coding schemes, the present approach is based on a systems level of analysis. ¹ 1 See Rogers ( 9 ) for a detailed review and critique of existing interaction coding schemes.
     

A Monte Carlo study of the effects of correlated method variance in moderated multiple regression analysis

It has been suggested that hierarchical regression analysis provides an unambiguous conclusion with regard to the existence of moderator effects (Arnold & Evans, 1979). This paper examines the impact of correlated error among the dependent and independent variables in order to explore whether or not artificial interaction terms can be generated. A Monte Carlo study was performed to investigate the effects of correlated error on noninteraction and interaction models. The results are clear-cut. Artifactual interaction cannot be created; true interactions can be attentuated. Some practical suggestions are provided for drawing inferences from hierarchical regression analysis.     

Maximum likelihood estimation of latent interaction effects with the LMS method

In the context of structural equation modeling, a general interaction model with multiple latent interaction effects is introduced. A stochastic analysis represents the nonnormal distribution of the joint indicator vector as a finite mixture of normal distributions. The Latent Moderated Structural Equations (LMS) approach is a new method developed for the analysis of the general interaction model that utilizes the mixture distribution and provides a ML estimation of model parameters by adapting the EM algorithm. The finite sample properties and the robustness of LMS are discussed. Finally, the applicability of the new method is illustrated by an empirical example. This research has been supported by a grant from the Deutsche Forschungsgemeinschaft, Germany, No. Mo 474/1 and Mo 474/2. The data for the empirical example have been provided by Andreas Thiele of the University of Frankfurt, Germany. The authors are indebted to an associate editor and to three anonymous reviewers ofPsychometrika whose comments and suggestions have been very helpful.     

Dynamic Creative Interaction Networks and Team Creativity Evolution: A Longitudinal Study

To assess the dynamical effects of creative interaction networks on team creativity evolution, this paper elaborates a theoretical framework that links the key elements of creative interaction networks, including node, edge and network structure, to creativity in teams. The process of team creativity evolution is divided into four phases, including formation, growth, maturity and decline/restart. The importance of domain‐relevant knowledge, creativity‐relevant skill, interaction frequency, interaction length, network density and closeness centrality are emphasized in specific phases of team creativity evolution in a complex creative context. To test our assumptions, a longitudinal study of creative teams in a “Challenge Cup” Creative Business Plan Competition for university students is performed and the full networks of 17 creative entrepreneur teams are mapped. Both static comparison and dynamic analysis are conducted to analyze the relationship between creative interaction networks and team creativity evolution. For specific phases of team creativity evolution, we find confirmation of our predictions. The implications of dynamic creative interaction networks for all the phases of creative teams from formation to decline/restart are discussed.