The implicit relational assessment procedure: emerging reliability and validity data

The Implicit Relational Assessment Procedure (IRAP) is a measure of ‘implicit cognition' developed on the basis of a contemporary behavioural analysis of language and cognition. The IRAP has now been applied to a range of foci over five years of published research. A frequently-cited caveat in publications to date is the need for further research to gauge the reliability and validity of the IRAP as an implicit measure. This review paper will provide a critical synthesis of available evidence for reliability and validity. The review applies a multifaceted test-theory approach to validity, and reliability is assessed through meta-analysis of published data. The discussion critically considers reviewed IRAP evidence with reference to the extant literature on alternative implicit measures, limitations of studies to date, and consideration of broader conceptual issues

The prevalence and characteristics of relational depth events in psychotherapy

We introduce two complementary measures of relational depth, defined as a state of profound contact and engagement between client and therapist. Using an internet-based survey of client and therapist accounts (n = 342), judges rated relational depth as present in over a third of significant therapy event descriptions. Participants also completed the Relational Depth Inventory (RDI), for which we report reliability, validity and factor structure. Relational depth events were more likely to occur in the presence of strong therapeutic alliance, and with female participants, but client or therapist role and therapy duration were not related to relational depth content or RDI. RDI items for connectedness, love, respect and intimacy were most strongly associated with relational depth content

Field spectroradiometer data : acquisition, organisation, processing and analysis on the example of New Zealand native plants : a thesis presented in fulfilment of the requirements for the degree of Master of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand

The purpose of this research was to investigate the acquisition, storage, processing and analysis of hyperspectral data for vegetation applications on the example of New Zealand native plants. Data covering the spectral range 350nm-2500nm were collected with a portable spectroradiometer. Hyperspectral data collection results in large datasets that need pre-processing before any analysis can be carried out. A review of the techniques used since the advent of hyperspectral field data showed the following general procedures were followed: 1. Removal of noisy or uncalibrated bands 2. Data smoothing 3. Reduction of dimensionality 4. Transformation into feature space 5. Analysis techniques Steps 1 to 4 which are concerned with the pre-processing of data were found to be repetitive procedures and thus had a high potential for automation. The pre-processing had a major impact on the results gained in the analysis stage. Finding the ideal pre-processing parameters involved repeated processing of the data. Hyperspectral field data should be stored in a structured way. The utilization of a relational database seemed a logical approach. A hierarchical data structure that reflected the real world and the setup of sampling campaigns was designed. This structure was transformed into a logical data model. Furthermore the database also held information needed for pre-processing and statistical analysis. This enabled the calculation of separability measurements such as the JM (Jeffries Matusila) distance or the application of discriminant analysis. Software was written to provide a graphical user interface to the database and implement pre-processing and analysis functionality. The acquisition, processing and analysis steps were applied to New Zealand native vegetation. A high degree of separability between species was achieved and using independent data a classification accuracy of 87.87% was reached. This outcome required smoothing, Hyperion synthesizing and principal components transformation to be applied to the data prior to the classification which used a generalized squared distance discriminant function. The mixed signature problem was addressed in experiments under controlled laboratory conditions and revealed that certain combinations of plants could not be unmixed successfully while mixtures of vegetation and artificial materials resulted in very good abundance estimations. The combination of a relational database with associated software for data processing was found to be highly efficient when dealing with hyperspectral field data

Business-oriented Analysis of a Social Network of University Students

Despites the great interest caused by social networks in Business Science, their analysis is rarely performed both in a global and systematic way in this field: most authors focus on parts of the studied network, or on a few nodes considered individually. This could be explained by the fact that practical extraction of social networks is a difficult and costly task, since the specific relational data it requires are often difficult to access and thereby expensive. One may ask if equivalent information could be extracted from less expensive individual data, i.e. data concerning single individuals instead of several ones. In this work, we try to tackle this problem through group detection. We gather both types of data from a population of students, and estimate groups separately using individual and relational data, leading to sets of clusters and communities, respectively. We found out there is no strong overlapping between them, meaning both types of data do not convey the same information in this specific context, and can therefore be considered as complementary. However, a link, even if weak, exists and appears when we identify the most discriminant attributes relatively to the communities. Implications in Business Science include community prediction using individual data.Social Networks; Business Science; Cluster Analysis; Community Detection; Community Comparison; Individual Data; Relational Data

Development and Validation of a Facebook Relational Maintenance Measure

This manuscript details the construction of a measure of Facebook relational maintenance behaviors. The first study generated an item pool by drawing from previous qualitative investigations and adapting an established relational maintenance scale. Participants were then invited to evaluate these items in order to establish face validity. During study two, participants were asked how often they used the behaviors represented in these items to maintain a specific friendship. Exploratory factor analysis was conducted to determine the underlying structure of these items; three latent factors emerged, social contact, response-seeking, and relational assurances. This factor structure was then assessed using confirmatory factor analysis during phase three. Study three participants were also asked to complete measures of friendship quality, Facebook intensity, and online social communication. The relationship of the three factors of Facebook relational maintenance to friendship quality, Facebook intensity, and online social communication suggests convergent and discriminant validity for the Facebook relational maintenance measure