Data‐driven approaches to information access

This paper summarizes three lines of research that are motivated by the practical problem of helping users find information from external data sources, most notably computers. The application areas include information retrieval, text categorization, and question answering. A common theme in these applications is that practical information access problems can be solved by analyzing the statistical properties of words in large volumes of real world texts. The same statistical properties constrain human performance, thus we believe that solutions to practical information access problems can shed light on human knowledge representation and reasoning.     

Trait tendencies to forgive,punish, and exonerate oneself: A multi-study investigation

In two studies (N’s = 515 and 359), we examine the utility of the Dual-Process Model of Self-Forgiveness for conceptualizing and measuring trait self-forgiveness with South African and American adults. Exploratory and confirmatory factor analyses of the trait-adapted Dual-Process Self-Forgiveness Scale supported an eight-item, two-factor structure of self-forgivingness consistent with prior theorizing and empirical evidence. The trait-adapted measure predicted incrementally more variance in outcomes beyond an existing measure of trait self-forgiveness. Latent subgroups characterized by tendencies to forgive, punish, and exonerate oneself in response to wrongdoing or failure were identified. Comparisons of mean differences provided preliminary evidence supporting the validity of the subgroups. Implications of the findings for conceptualizing and measuring self-forgiveness are discussed.     

Mood and memories of parental rearing styles: A comparison of mood effects on questionnaire-cued and free recall of autobiographical memories

The relationship between memories of childhood experiences (e.g., adverse parenting) and adult depression often found raises questions of interpretation. On the one hand, both laboratory studies and clinicians' experiences suggest that subjects in a depressed mood frequently show a negative bias in perceptions and memories. Negative childhood memories in depressed persons might, therefore, be interpreted as epiphenomena of depressed mood instead of etiological factors. On the other hand, memories of childhood experiences seem remarkably stable across changes in depressed mood, especially when memories are elicited by means of standardized questionnaires. In the mood and memory literature several explanations for this stability are offered. For one thing, highly structured cues to elicit memories (such as in questionnaires) are hypothesized to be less susceptible to mood bias than unstructured memory cues (such as in free recall procedures). On the other hand, resource allocation theorists suggest that childhood memories, being well established and rehearsed, are relatively impervious to mood bias no matter how they are elicited. In this study we examined whether different methods of eliciting childhood memories (i.e., free recall and questionnaire-cued) are differentially susceptible to mood bias. To this aim, we used a mood induction procedure to induce depressed, neutral, and elated mood and assessed childhood memories both before and after the mood induction using both questionnaires and free recall to elicit memories. Results suggested that memories elicited by means of free recall as well as by means of questionnaire-cued recall were susceptible to depressed and elated mood bias. The implications for research addressing the link between childhood experiences and depression are discussed.     

Constrained classification: The use of a priori information in cluster analysis

In many classification problems, one often possesses external and/or internal information concerning the objects or units to be analyzed which makes it appropriate to impose constraints on the set of allowable classifications and their characteristics. CONCLUS, or CONstrained CLUStering, is a new methodology devised to perform constrained classification in either an overlapping or nonoverlapping (hierarchical or nonhierarchial) manner. This paper initially reviews the related classification literature. A discussion of the use of constraints in clustering problems is then presented. The CONCLUS model and algorithm are described in detail, as well as their flexibility for use in various applications. Monte Carlo results are presented for two synthetic data sets with appropriate discussion of the resulting implications. An illustration of CONCLUS is presented with respect to a sales territory design problem where the objects classified are various Forbes-500 companies. Finally, the discussion section highlights the main contribution of the paper and offers some areas for future research.We wish to thank C. Mallows and J. D. Carroll for some helpful technical discussion and L. Clark and D. Art for their valuable computer assistance. We also wish to thank H. Pollak, R. Gnanadesikan, and J. Kettenring for their thorough reviews of a previous draft of this paper. Finally, we acknowledge helpful comments of the editor and two anonymous reviewers.     

Empirical tests of scientific realism: A quantitative framework

The scientific realism debate in philosophy of science raises some intriguing methodological issues. Scientific realism posits a link between a scientific theory's observational and referential success. This opens the possibility of testing the thesis empirically, by searching for evidence of such a link in the record of theories put forward in the history of science. Many realist philosophers working today propose case study methodology as a way of carrying out such a test. This article argues that a qualitative method such as case study methodology is not adequate for this purpose, for two reasons: to test scientific realism is to pose an effects-of-causes question, and observational and referential success are quantities that theories possess to a greater or lesser degree. The article concludes that an empirical test of scientific realism requires a quantitative method.     

Typical coping patterns: A person-centered approach to coping

Applying the person-oriented approach to coping can provide a unique perspective by revealing typical latent patterns. The study examined typical latent patterns shown by task-, emotion- and avoidance-oriented trait-based coping styles. We performed secondary analyses with Latent Profile Analysis on our former data and three independent datasets containing coping measures with the CISS-48 scale (Endler & Parker, 1994). Gender differences were also studied. Two basic profiles seem universal: the first is characterized by a high level of task-oriented coping, and the second profile is with moderately high scores on all three coping styles. The finding indicates that two fundamental latent coping profiles exist, which suggests a within-subject comparison in practice instead of investigating the absolute value of coping styles. Comparing data before and after 2020, COVID-19 does not seem to affect these profiles. The person-centered approach provides a possibility for the integration of coping-related findings.     

Understanding physical education teachers’ motivational outcomes and feasibility beliefs to implement motivational strategies: The role of perceived pressures from a variable- and person-centered approach

Work-related pressures perceived by PE teachers have been suggested to affect their motivation and behaviour. This study aims to contribute to the existing literature on this topic. Through two different objectives. First, the role of perceived pressures in the prediction of teachers’ motivation and, in turn, on their feasibility beliefs to implement motivational strategies is tested through a SEM. Secondly, PE teachers’ profiles according to the different types of perceived pressures are established and compared in terms of motivational outcomes and feasibility beliefs. A total of 217 PE teachers completed validated questionnaires. Results showed that, as hypothesized, pressures negatively predicted needs satisfaction, which, in turn, positively predicted feasibility beliefs. Three profiles emerged in the cluster analysis. Regarding to these profiles, teachers who were low on perceived pressures displayed the most adaptive pattern; teachers who reported high time constraints pressures underline the detrimental role that this type of pressure plays on both teacher and teaching outcomes. Implications for educational policy and practice are discussed.     

Ten principles of counselling: Grounded theory in a mixed methods research project

This grounded theory (GT) study is part of an extensive mixed methods research project to develop general principles for counselling. Nine hundred counselling protocols were examined. The material is characterised by a quantitative amount of data with a qualitative character. The research is based on the systemic-constructivist research paradigm. The variety of perspectives is an important aspect of the work. Therefore, it is important that the entire research process with GT takes place in exchange with other research settings in order to minimise the limitations caused by the perspective of the researchers. During the research process, there was an intensive examination of GT in order to adapt it to the research project. By using the GT to analyse the extensive qualitative material, the actions of the counsellors were observed and 10 counselling principles were developed. This article presents the research process and the findings.     

Extending exploratory diagnostic classification models: Inferring the effect of covariates

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.