bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clustering and Classification Methods
Authors: Milligan Glenn W. , Hirtle Stephen C. 
Published: November 2003

AbstractThe chapter by Milligan and Hirtle provides an overview of the current state of knowledge in the field of clustering and classification. Such methods are used to find groups in multivariate data sets. The methods are discussed within the context of exploratory data analysis, though some confirmatory or testing methods are reviewed. A survey of the issues critical to the analysis of empirical data is presented along with “best practice” recommendations for the applied user. Coverage includes sections on data preparation, data models, and data representation using distance and similarity measures. The section on clustering algorithms covers a wide range of classification methods. In addition, the algorithms section includes a discussion of the known cluster recovery performance of various selected clustering methods. The fourth section covers a variety of issues important for applied analyses such as data sampling, variable selection, variable standardization, choosing the number of clusters, and postclassification analysis of the results. Threaded into the discussion are three example applications of the methodology to empirical data. The examples are based on perceived kinship data, animal similarity data, and the classification of single malt scotch whiskies.
Clinical Forensic Psychology
Authors: Douglas Kevin S. , Otto Randy K. , Borum Randy 
Published: November 2003

AbstractClinical forensic psychology can be defined as assessment, treatment, or consultation that revolves around clinical issues occurring in legal contexts. This chapter discusses methodological challenges and approaches that arise in three primary areas of clinical forensic psychology—descriptive forensic assessment (e.g., psycholegal capacities), predictive assessment (e.g., violence risk assessment), and evaluation of legally relevant constructs (e.g., psychopathic personality and malingering). Forensic assessment research is challenged by varying legal definitions, lack of gold standards for validation, insensitive predictor and outcome measures, dichotomous legal outcomes versus continuous psychological outcomes, low and varying outcome base rates, ethical constraints placed upon researchers by legal realities, and studying constructs defined partially by deceitfulness. Research of descriptive questions include literature surveys, polling experts, theory‐based development, and evaluating agreement between test classification and clinicians' assessments. Although postdictive and pseudoprospective designs are common in predictive research, they have shortcomings best overcome through use of prospective designs that include multiple measures and sources of data. Repeated‐measures prospective designs, case‐crossover designs, and naturally occurring field experiments offer novel opportunities to study predictive questions. Research on psycholegal constructs commonly relies on factor analytic and prototypical approaches to explicating psycholegal constructs. Simulation and analogue studies are often used to study constructs such as malingering.
Psychotherapy Outcome Research
Authors: Behar Evelyn S. , Borkovec Thomas D. 
Published: November 2003

Abstract

Between‐group outcome research is a scientific approach to evaluating the effectiveness of psychotherapy and the mechanisms of change associated with those treatments for psychological disorders. This area of research is replete with important methodological issues that need to be considered in order for investigators to draw the strongest, most specific cause‐and‐effect conclusions about the active components of treatments, human behavior, and the effectiveness of therapeutic interventions. In this chapter, we present the various methodological considerations associated with these experiments. The chapter begins with a description of the different experimental designs from which investigators may choose in designing a therapy outcome study. These designs include the no‐treatment and common factors comparison designs, as well as the dismantling, additive, catalytic, and parametric designs. We also present the methodological, client/participant, and therapist concerns that must be taken into account in the design stage of a treatment outcome investigation. Following this, we discuss the measurement of change, starting with the considerations surrounding dependent variables and ending with methods of analyzing data and assessing clinically significant change. Finally, after a presentation on small‐
N
experimental designs, we discuss the importance of scientific research in naturalistic settings.
Health Psychology
Authors: Smith Timothy W. 
Published: November 2003

AbstractThis chapter reviews methodological issues in health psychology. The field addresses three major topics—health behavior and risk reduction (e.g., smoking cessation), stress and health or psychosomatics (e.g., emotions and cardiovascular disease), and psychosocial aspects of medical illness and care (e.g., adjustment to cancer and adjunctive psychosocial treatments). In each of these areas, research focuses on the determinants of risk and maladaptation, psychosocial and psychobiological connections with physical health, and the evaluation of interventions intended to reduce risk and improve functioning and quality of life. These very different research topics require a wide range of designs and methods, especially in light of the need to consider specific medical populations, standard medical care, and related topics in pathophysiology, epidemiology, and health economics. Classic methodological issues in psychology involving measurement, design, and analysis are critical in the design and evaluation of health psychology research, but the translation of these traditional topics into issues of physical health and illness poses major challenges.
Program Evaluation
Authors: Mark Melvin M. 
Published: November 2003

AbstractThis chapter focuses on the evaluation of policies and programs. Given the importance of causal questions, the chapter's major focus is on methods for estimating the effects of policies and programs in the context of evaluation. Topics addressed include the concept of causality, experimental and quasi‐experimental designs and analysis, including techniques for dealing with attrition and with selection bias, tests of mediation, and methods of “principled discovery”, that is, the integration of exploratory methods with subsequent tests of discoveries. Given that evaluations sometimes emphasize issues other than causal questions, this chapter attends in part to other kinds of methods that may be used for evaluation. In particular, the methods, potential, and limits of performance measurement are discussed. In addition, because evaluators may need special skills to identify the right evaluation questions, attention is given to the analysis of stakeholder needs and to a potentially useful policy analytic perspective. Finally, possible future developments in the field of evaluation are discussed, including areas of potential methodological development and challenges to be faced.
Mood Measurement: Current Status and Future Directions
Authors: Watson David , Vaidya Jatin 
Published: November 2003

AbstractThis chapter provides a basic introduction to the measurement of mood. We begin by defining the concept of mood and distinguishing it from related constructs such as emotion. We then discuss recent data related to the underlying structure of mood; on the basis of this evidence, we propose a hierarchical model consisting of two higher‐order dimensions (alternatively conceptualized as either pleasantness/activation or positive affect/negative affect) and multiple lower‐order states (e.g., fear, anger, sadness, joy). Next, we use this organizing scheme to evaluate existing mood instruments, noting both strengths and weaknesses in the available measures. Among other things, our review suggests the need for better measurement of the higher‐order activation dimension and for a more compelling taxonomy at the lower‐order level. We then examine several general issues that are broadly relevant to the overall construct validity of mood measures, focusing on such potential problems as: (1) random measurement error, (2) social desirability, and (3) the acquiescence response bias. Finally, we focus specifically on scales assessing long‐term individual differences in affectivity, reviewing such issues as: (1) temporal stability, (2) self‐peer agreement, and (3) the construct validity of general versus aggregated measures of trait affect.
Measuring Personality and Psychopathology
Authors: Morey Leslie C. 
Published: November 2003

AbstractContemporary scale construction methods focus heavily on a construct validation approach that elaborates the extent to which a measure can be said to reflect a theoretical (and hence not directly observable) construct. The construct validation approach emphasizes the interplay between the theoretical elaboration of the construct and its methodological validation.Three stages define, in general, the development of a scale using the construct validation approach. The stage oftheory formulationinvolves an explication of the content domain of the construct, a delineation of the nature of the classification model and the linkages between constructs in the model, and a specification of the relationship of constructs to external variables, such as etiology or treatment outcome. The second stage ofinternal validationinvolves the operationalization of the constructs and examining various internal properties of the classification, such as interrater reliability, stability of measurement over occasions, internal correlation matrices, or the replicability of classification or factorial structures across different samples. The third stage of construct validation involvesexternal validation. At this stage, links of the constructs to other variables related to etiology, course, or prediction must be tested. This process involves both convergent and discriminant validation.This chapter surveys important issues and procedures in the construction and implementation of measures of personality and psychopathology. Specific issues at each stage of scale development are discussed, and the methods of the construct validation approach are contrasted with other scale construction methods.
The Circumplex Model: Methods and Research Applications
Authors: Gurtman Michael B. , Pincus Aaron L. 
Published: November 2003

AbstractCircumplex models assume that the content and structure of a particular domain (e.g., personality, affect, vocational interests) can be meaningfully described in terms of a circular array of variables. With the growing popularity of such models in psychology comes the need to apply appropriate research methodologies and data‐analytic strategies, as well as to further develop relevant research applications. The purpose of this chapter then is to review these methods and their associated applications. The chapter is organized with reference to basic empirical questions likely to underlie investigators' applications of the model and its related methods. Thus, after the circumplex is defined and examples from contemporary psychological literature are described, the chapter is divided into sections addressing the following questions: (a) How do I evaluate circular representations in my domain of interest? (b) How do I use the circumplex to describe individuals? (c) How do I use the circumplex to describe and compare groups? (d) How do I use the circumplex to evaluate constructs and their measures.? We conclude the chapter with a brief discussion of future directions in the development and application of circumplex methodology.
Item Response Theory and Measuring Abilities
Authors: Schmidt Karen M. , Embretson Susan E. 
Published: November 2003

Abstract
This chapter covers ability measurement in its two dominant traditions: Classical Test Theory and Item Response Theory. CTT methods are based largely on large sampling norms, fixed‐length tests, and ordinal‐level measurement scales. A person's ability performance is based upon relative standing among a population of individuals for a given test. However, a person's performance, or its change, relative to particular item performance, is not well incorporated. IRT methods use model‐based measurement, in which person performance may be referenced to specific item parameters, as well as to relative standing in a population of persons. The meaningfulness of the item parameters is independent of the population of persons sampled. Persons can receive different tests, of varying lengths, and still be meaningfully compared. The scale of measurement for IRT is interval‐level, so relative score levels and their change are weighted accordingly. This chapter discusses methodological assumptions, item difficulty, score meaning, test construction, and test evaluation using classical and item response theory approaches. Simple and complex models are presented, and each method's strengths and weaknesses are discussed.
Multiple Linear Regression
Authors: Aiken Leona S. , West Stephen G. , Pitts Steven C. 
Published: November 2003

AbstractMultiple regression analysis (MR) is a highly flexible system for examining the relationship of a collection of independent variables (or predictors) to a single dependent variable (or criterion). The independent variables may be quantitative (e.g., personality traits, family income) or categorical (e.g., ethnic group, treatment conditions in an experiment). The present chapter explores ordinary least‐squares regression with a continuous dependent variable. The chapter has two foci: (1) testing of theoretical predictions through multiple regression and (2) identification of problems with implementation of regression analysis, both from the perspectives of model specification and the data themselves. The structure of MR is described, including the regression equation, estimation of partial regression coefficients, measures of overall model fit, and the contribution of individual predictors and sets of predictors to prediction accuracy. The treatment of categorical predictors through effects, dummy, and contrast coding is explained. Polynomial regression for capturing curvilinear relationships is explored. The specification and testing of interactions between continuous variables and between a continuous and a categorical variable are explicated. Assumptions of MR and detection of violations are explained, as is the use of regression diagnostics to identify problematic cases. The chapter illustrates the interplay between theory and empirical findings in the specification, testing, and revision of regression models.
Logistic Regression
Authors: DeMaris Alfred 
Published: November 2003

AbstractThis chapter provides a thorough introduction to logistic regression models for categorical response variables. When the response is binary, the use of OLS regression is problematic for two reasons: (1)the errors are inherently heteroscedastic and (2)the left‐hand side of the equation is subject to a constraint—confinement within the bounds of 0 and 1—that doesn't apply to the right‐hand side. Logistic regression solves these problems by modeling the log odds of being in a given category ofYas a linear function of the regressors, providing parameter estimates via the technique of maximum likelihood. As the binary‐response case is most often encountered in practice, substantial discussion is devoted to its explication, covering such topics as analogues ofthe comparison of models across groups, and the comparison of coefficients across nested models. Logistic regression extends in a straightforward fashion to response variables with more than two categories. Multinomial logistic regression is best suited to a polytomous response with unordered values, whereas the ordered logit model is ideal for an ordinal response. Connected with these latter two approaches are a test for the collapsibility of response categories, as well as a test for the appropriateness of the ordered logit model.
Survival Analysis
Authors: Singer Judith D. , Willett John B. 
Published: November 2003

AbstractPsychologists studying whether and when events occur face unique design and analytic difficulties. The fundamental problem is how to handle censored observations, the people for whom the target event does not occur before data collection ends. The methods of survival analysis overcome these difficulties and allow researchers to describe patterns of event occurrence, compare these patterns among groups, and build statistical models of the risk of event occurrence over time. In this chapter, we present a nonmathematical introduction to survival analysis. After describing the basic concepts, we focus on two topics‐study design and data analysis‐and for each, we identify the key issues researchers face and we provide guidelines for making informed decisions about them. In the process, we review how psychologists have used the methods to date and point towards new directions for their application.
Latent Class and Latent Transition Analysis
Authors: Lanza Stephanie T. , Flaherty Brian P. , Collins Linda M. 
Published: November 2003

AbstractOften quantities of interest in psychology cannot be observed directly. These unobservable quantities are known as latent variables. By using multiple items as indicators of the latent variable, we can obtain a more complete picture of the construct of interest and estimate measurement error. One approach to latent variable modeling is latent class analysis, a method appropriate for examining the relationship between discrete observed variables and a discrete latent variable. The present chapter will introduce latent class analysis, its extension to repeated measures, and recent developments further extending the latent class model. First, the concept of a latent class and the mathematical model are presented. This is followed by a discussion of parameter restrictions, model fit, and the measurement quality of categorical items. Second, latent class analysis is demonstrated through an examination of the prevalence of depression types in adolescents. Third, longitudinal extensions of the latent class model are presented. This section also contains an empirical example on adolescent depression types, where the previous analysis is extended to examine the stability and change in depression types over time. Finally, several recent developments that further extend the latent class model are introduced.
Evolutionary Psychology
Authors: Durrant Russil , Ellis Bruce J. 
Published: November 2003

AbstractIn this chapter we provide an introduction to the field of evolutionary psychology and describe the methodology that evolutionary psychologists use to explain human cognition and behavior. This description begins at the broadest level with a review of basic, guiding assumptions that are consensually held by most evolutionary scientists, including concepts of natural selection, adaptation, sexual selection, and inclusive fitness. Special attention is paid to the question of how to distinguish adaptations from nonadaptations in the biological world. Next, we discuss special metatheoretical assumptions employed by evolutionary psychologists, including the emphasis on psychological mechanisms as the main unit of analysis, domain‐specificity of psychological mechanisms, and the environment of evolutionary adaptedness. We then illustrate how evolutionary psychologists apply these assumptions to develop more specific theoretical models, hypotheses, and predictions that are tested in research. We use examples of sex and mating, including discussion of parental investment theory, and good genes sexual selection theory to demonstrate how evolutionary psychological theories are developed and tested. Specific hypotheses and predictions from good genes sexual selection theory are evaluated. We conclude by discussing potential future directions in evolutionary psychology and its potential impact on psychological inquiry.
Comparative Psychology of Vision
Authors: Jacobs Gerald H. 
Published: November 2003

AbstractLight provides a very rich source of information that many animals can use in the guidance and control behavior. Considerable progress has been made in understanding how this comes about through the comparison of visual capacities and visual biology across widely variant species. This chapter summarizes that progress by first considering some basic features of eyes and examining how these features can be exploited to allow vision in different photic environments. Comments are then offered on the means that have been developed for assessing animal vision and on the challenges inherent in inferring visual capacity from anatomy and physiology. Finally, a range of examples are offered as to how animals solve three visual problems: detecting change in the photic environment, resolving spatial structure, and exploiting chromatic cues.
Comparative Psychology of Audition
Authors: Moss Cynthia F. , Carr Catherine E. 
Published: November 2003

AbstractMany animal species use sound to orient in the environment, detect predators, and communicate with conspecifics. Auditory systems have evolved to enable these behaviors, permitting the detection, localization, and discrimination of acoustic events. This chapter provides an overview of auditory systems and behavior in a variety of animal species, including insects, amphibians, birds, and mammals. Emphasizing a comparative and evolutionary perspective, the chapter details features of the auditory pathway—from the ear to central structures involved in the processing of acoustic stimuli. Anatomical pathways and physiological response characteristics of neurons at different stages of the auditory pathway are described. The chapter also reviews behavioral measures of auditory function, emphasizing studies of absolute hearing sensitivity, frequency selectivity, temporal resolution, sound localization, and scene analysis. Comparisons of auditory system anatomy, physiology, and function across species reveal exquisite specializations but also many common principles across the animal kingdom.
Comparative Psychology of Motor Systems
Authors: Fouad Karim , Fischer Hanno , Büschges Ansgar 
Published: November 2003

AbstractResearch in the field of locomotor control has greatly benefited from studies in lower vertebrates and invertebrates. These model systems helped to unravel basic principles for locomotor pattern generation from the systems down to the subcellular level, indicating that a functional locomotor program results from a close interaction between central pattern‐generating networks with peripheral signals from appendages as well as coordinating signals from adjacent segments. At present, it is clear that most of the mechanisms described also contribute to locomotor pattern generation in higher vertebrates, including mammals. Therefore, it currently appears that common principles underlie the design of locomotor networks in the entire animal kingdom. This review will give an historic overview of the research in this field and summarize in a comparative manner the current knowledge on several aspects of pattern generating networks for locomotion, including the construction principles and location of pattern‐generating networks for locomotion, the role of sensory signals, plasticity in motor systems, and modulation of locomotor activity. A major outcome of recent research is that networks are by no means “hardwired” but instead flexible regarding their topology, and the properties of the connections between their components.