The seemingly quixotic pursuit of a cumulative psychological science: Introduction to the special issue.

The goal of any empirical science is to pursue the construction of a cumulative base of knowledge upon which the future of the science may be built. However, there is mixed evidence that the science of psychology can accurately be characterized by such a cumulative progression. Indeed, some argue that the development of a truly cumulative psychological science is not possible with the current paradigms of hypothesis testing in single-study designs. The author explores this controversy as a framework to introduce the 6 articles that make up this special issue on the integration of data and empirical findings across multiple studies. The author proposes that the methods and techniques described in this set of articles can significantly propel researchers forward in their ongoing quest to build a cumulative psychological science. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis.

There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the pooling of data drawn from multiple existing developmental studies. This approach is characterized by a host of advantages, but this also introduces several new complexities that must be addressed prior to broad adoption by developmental researchers. In this article, the authors focus on methods for fitting measurement models and creating scale scores using data drawn from multiple longitudinal studies. The authors present findings from the analysis of repeated measures of internalizing symptomatology that were pooled from three existing developmental studies. The authors describe and demonstrate each step in the analysis and conclude with a discussion of potential limitations and directions for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Integrative data analysis: The simultaneous analysis of multiple data sets.

There are both quantitative and methodological techniques that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis, which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. However, when the original data can be obtained from multiple studies, many advantages stem from the statistical analysis of the pooled data. The authors define integrative data analysis (IDA) as the analysis of multiple data sets that have been pooled into one. Although variants of IDA have been incorporated into other scientific disciplines, the use of these techniques is much less evident in psychology. In this article the authors present an overview of IDA as it may be applied within the psychological sciences, discuss the relative advantages and disadvantages of IDA, describe analytic strategies for analyzing pooled individual data, and offer recommendations for the use of IDA in practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to the special section on integrative approaches to source memory.

Source memory involves recognizing or recalling particular features that cause us to think of a mental experience as representing a particular past event. Research directed at understanding source memory comes from many domains, including cognitive-behavioral psychology, neuroscience, lifespan psychology, clinical psychology, and psychiatry. The authors believe that integrating multiple approaches to investigating source memory--most notably here, cognitive-behavioral and cognitive neuroscience--provides productive cross-fertilization of theory, methods, and analyses that in turn will yield unique constraints to our understanding of human memory. The investigations in this special section demonstrate the promise of such an integrative approach for advancing theories of source memory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to the special issue on alternative methods of data interpretation.

Introduces this special issue of Canadian Journal of Experimental Psychology (2003, Vol 57[3]) which focuses on alternative methods of data interpretation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to the special issue: Integrating personality and social psychology.

The purpose of this Special Issue is not to mark one approach as superior to others but rather to present a wide variety of approaches to the problem of integrating personality and social psychology. This introductory essay seeks to underscore some of the integrative aspects of the articles collected here and to help point the direction for future activity along the same lines. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to the special issue on object perception and scene analysis.

Work in psychophysics, neuroscience, and visual perception has led to significant recent advances in our understanding of the human visual system. The articles in this special issue represent diverse approaches to the study of visual perception and highlight some of these recent advances, as applied to issues involved in understanding object perception and scene analysis. The special issue grew out of a workshop on object and scene perception held in September 1990 at the University of Leuven. The intent of the original workshop and of the collection of articles in this issue is to bring together the work of researchers using widely differing techniques and theoretical frameworks, and to apply recent results to the issues surrounding object and scene perception. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Research methods for profile analysis: Introduction to the special issue.

The five articles that make up the bulk of this special issue collectively illustrate new methods for examining the utility of sub-test and scale-level profile analysis. By necessity they are preliminary, but they illustrate methods that may indeed prove useful and may help resolve this divide between research and practice. This special issue grew out of a symposium conceived and organized by Cecil Reynolds at the American Psychological Association in 1998. The articles and one of the commentaries in this special issue are extensions of that symposium. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Sibling relationship contributions to individual and family well-being: introduction to the special issue.

This special issue presents new findings that illustrate the ways in which sibling relationships serve as important contexts for individual development and family functioning. This collection of articles, which emphasizes effects on both normative and at-risk development, is intended to stimulate further research on the multifaceted and often contradictory contributions siblings extend to one another across the life course. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models.

[Correction Notice: An erratum for this article was reported in Vol 14(4) of Psychological Methods (see record 2009-22665-007). In this article, the authors wrote, "To our knowledge, the multisample framework is the only available option within these [latent variable] programs that allows for the moderation of all types of parameters, and this approach requires a single categorical moderator variable to define the samples.” Bengt Muthén has clarified for the authors that some programs, including Mplus and Mx, can allow for continuous moderation through the implementation of nonlinear constraints involving observed variables, further enlarging the class of MNLFA models that can be fit with these programs.] When conducting an integrative analysis of data obtained from multiple independent studies, a fundamental problem is to establish commensurate measures for the constructs of interest. Fortunately, procedures for evaluating and establishing measurement equivalence across samples are well developed for the linear factor model and commonly used item response theory models. A newly proposed moderated nonlinear factor analysis model generalizes these models and procedures, allowing for items of different scale types (continuous or discrete) and differential item functioning across levels of categorical and/or continuous variables. The potential of this new model to resolve the problem of measurement in integrative data analysis is shown via an empirical example examining changes in alcohol involvement from ages 10 to 22 years across 2 longitudinal studies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

"Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models": Clarification to Bauer and Hussong (2009).

Reports a clarification to "Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models" by Daniel J. Bauer and Andrea M. Hussong (Psychological Methods, 2009[Jun], Vol 14[2], 101-125). In this article, the authors wrote, "To our knowledge, the multisample framework is the only available option within these [latent variable] programs that allows for the moderation of all types of parameters, and this approach requires a single categorical moderator variable to define the samples.” Bengt Muthén has clarified for the authors that some programs, including Mplus and Mx, can allow for continuous moderation through the implementation of nonlinear constraints involving observed variables, further enlarging the class of MNLFA models that can be fit with these programs. (The following abstract of the original article appeared in record 2009-08072-001.) When conducting an integrative analysis of data obtained from multiple independent studies, a fundamental problem is to establish commensurate measures for the constructs of interest. Fortunately, procedures for evaluating and establishing measurement equivalence across samples are well developed for the linear factor model and commonly used item response theory models. A newly proposed moderated nonlinear factor analysis model generalizes these models and procedures, allowing for items of different scale types (continuous or discrete) and differential item functioning across levels of categorical and/or continuous variables. The potential of this new model to resolve the problem of measurement in integrative data analysis is shown via an empirical example examining changes in alcohol involvement from ages 10 to 22 years across 2 longitudinal studies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An array of qualitative data analysis tools: A call for data analysis triangulation.

One of the most important steps in the qualitative research process is analysis of data. The purpose of this article is to provide elements for understanding multiple types of qualitative data analysis techniques available and the importance of utilizing more than one type of analysis, thus utilizing data analysis triangulation, in order to understand phenomenon more fully for school psychology research and beyond. The authors describe seven qualitative analysis tools: methods of constant comparison, keywords-in-context, word count, classical content analysis, domain analysis, taxonomic analysis, and componential analysis. Then, the authors outline when to use each type of analysis. In so doing, the authors use real qualitative data to help distinguish the various types of analyses. Furthermore, flowcharts and tables are provided to help delineate when to choose each type of analysis. Finally, the role of computer-assisted software in the qualitative data-analytic process is discussed. As such, use of the analyses outlined in this article should help to promote rigor in qualitative research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Assumptions of cross-level measurement and structural invariance in the analysis of multilevel data: Problems and solutions.

This article demonstrates assumptions of invariance that researchers often implicitly make when analyzing multilevel data. The first set of assumptions is measurement-based and corresponds to the fact that researchers often conduct single-level exploratory and confirmatory factor analyses, and reliability analyses, with multilevel data. The second assumption, that of structural invariance, is engineered into the common multilevel random coefficient model, in that such analyses impose structural invariance across multiple levels of analysis when lower-level relationships represent both between- and within-groups effects. The nature of these assumptions, and ways to address their tenability, are explored from a conceptual standpoint. Then an empirical example of these assumptions and ways to address them is provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Application of canonical correlational analysis to learning data.

Considers the application of Hotelling's canonical correlation analysis to certain problems of learning, such as (1) prediction of learning from external measures, (2) efficiency of learning indices as predictors of academic grades, (3) the extent to which different sets of learning scores share the same function, and (4) changes in the factorial structure of learning as practice continues. Analyses of the published data using this statistical method reveal that there is a considerable amount of improvement in predictive efficiency if learning is treated in multivariate terms. An important methodological point is the finding that in classical eyelidconditioning experiments, the Ss should be matched in terms of their reflex sensitivity to light and puff. It is also felt that canonical analysis may serve as an alternative method of studying the nature and extent of change in ability patterns as improvement occurs in a learning task. (51 ref.) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to the special issue on developments in psychological measurement and assessment.

This introduction to the special issue on developments in psychological measurement and assessment sets the special issue within a larger scientific and professional context and provides summaries of the 8 articles that make up the issue. The articles address (a) self-report personality and psychopathology assessment; (b) intensive, repeated, self-report measures used in naturalistic settings; (c) implicit measures of personality; (d) personality assessment and faking in personnel selection; (e) performance appraisals; (f) risk assessments; (g) intelligence and the assessment of intelligence; and (h) neuropsychological assessment. Together, these articles demonstrate the substantial progress being made in the research on psychological measurement and assessment and the solid foundation that this work provides for both psychological science and practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The proof of the pudding: An illustration of the relative strengths of null hypothesis, meta-analysis, and Bayesian analysis.

Some methodologists have recently suggested that scientific psychology's overreliance on null hypothesis significance testing (NHST) impedes the progress of the discipline. In response, a number of defenders have maintained that NHST continues to play a vital role in psychological research. Both sides of the argument to date have been presented abstractly. The authors take a different approach to this issue by illustrating the use of NHST along with 2 possible alternatives (meta-analysis as a primary data analysis strategy and Bayesian approaches) in a series of 3 studies. Comparing and contrasting the approaches on actual data brings out the strengths and weaknesses of each approach. The exercise demonstrates that the approaches are not mutually exclusive but instead can be used to complement one another. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measurement equivalence: A comparison of methods based on confirmatory factor analysis and item response theory.

Current interest in the assessment of measurement equivalence emphasizes 2 major methods of analysis. The authors offer a comparison of a linear method (confirmatory factor analysis) and a nonlinear method (differential item and test functioning using item response theory) with an emphasis on their methodological similarities and differences. The 2 approaches test for the equality of true scores (or expected raw scores) across 2 populations when the latent (or factor) score is held constant. Both approaches can provide information about when measurrment nonequivalence exists and the extent to which it is a problem. An empirical example is used to illustrate the 2 approaches. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using comparison data to differentiate categorical and dimensional data by examining factor score distributions: Resolving the mode problem.

Factor-analytic research is common in the study of constructs and measures in psychological assessment. Latent factors can represent traits as continuous underlying dimensions or as discrete categories. When examining the distributions of estimated scores on latent factors, one would expect unimodal distributions for dimensional data and bimodal or multimodal distributions for categorical data. Unfortunately, identifying modes is subjective, and the operationalization of counting local maxima has not performed very well. Rather than locating and counting modes, the authors propose performing parallel analyses of categorical and dimensional comparison data and calculating an index of the relative fit of these competing structural models. In an extensive Monte Carlo study, the authors replicated prior results for mode counting and found that trimming distributions' tails helped. However, parallel analyses of comparison data achieved much greater accuracy, improved base rate estimation, and afforded consistency checks with other taxometric procedures. Two additional studies apply this approach to empirical data either known to be categorical or presumed to be dimensional. Each study supports this new method for factor-analytic research on the latent structure of constructs and measures in psychological assessment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to the special issue: Psychology and AIDS.

From its beginnings in 1981, psychologists have been involved in worldwide efforts to meet the many challenges of the AIDS health crisis. As researchers and practitioners, as consultants and policy advisors, as educators and evaluators, and as activists and concerned members of the community--we psychologists have responded in many ways. We have attempted a broad perspective in approaching this special issue. We wanted coverage of the science, the practice, and the social issues that AIDS has brought into focus for psychology. Any attempt to cover all the issues is doomed to failure, however. The science changes too quickly, and the disease is spreading too rapidly, for any group of journal articles to ever claim currency and comprehensiveness. The issue has been divided into six major sections, each with an introduction. We begin with articles that overview the roles of psychology in the AIDS health crisis as seen in our own profession, and from various levels of science, health care, and legislative and government action. Then we present articles reviewing some of the primary issues AIDS commands us to examine: public health, antibody testing, AIDS and the communities of Black and Hispanic men, IV drug abuse, sexual behavior change, stigma, and psychoneuroimmunology. (PsycINFO Database Record (c) 2010 APA, all rights reserved)