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)     

Facilitating rigorous qualitative research in rehabilitation psychology.

Qualitative research methods have much to contribute to theoretical and applied knowledge in rehabilitation psychology. However, as a discipline, rehabilitation psychology has been behind the curve in employing qualitative methods. Objectives: This article is a summary of the state of qualitative research in rehabilitation and an introduction to various methodological dimensions to consider in implementing qualitative rehabilitation psychology research. Types and examples of qualitative rehabilitation research are presented. Criteria for evaluating qualitative research are discussed. Finally, the majority of this article is devoted to presenting the various methodological dimensions on which researchers must make decisions in designing and implementing rigorous qualitative research (e.g., paradigms, methods, data collection strategies, data analysis procedures, reliability/validity). Conclusions: Rehabilitation psychology has much to gain through qualitative research, and success in incorporating qualitative evidence will be ensured by rehabilitation psychologists learning and rigorously implementing qualitative methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using qualitative research to strengthen clinical practice.

Does qualitative research have the potential to be useful to practitioners? How might it improve the practice of professional psychology for clients and for practitioners? This article describes the qualitative research paradigm, discusses how it can be adapted to clinical practice, and provides an example of a qualitative study that practitioners can easily accomplish. (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)     

Le défrichement: Recherche qualitative en psychologie chez les anglophones canadiens.

This is a French version of the article that originally appeared in Canadian Psychology, 2002(August), Vol 43(3), p. 139-140. (The following abstract of the original article appeared in record 2002-17756-001). Qualitative research (QR) occupies a middle ground between the sciences and the humanities, which goes against established research practice in psychology and most related social and health science disciplines. At present, QR in Canadian psychology is beginning to take root in some universities and research organizations. Most of the contributors to this special issue reflect this development in Anglophone Canadian psychology. This article briefly introduces the contributions to this special issue. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Consensual Qualitative Research: An Update.

The authors reviewed the application of consensual qualitative research (CQR) in 27 studies published since the method's introduction to the field in 1997 by C. E. Hill, B. J. Thompson, and E. N. Williams (1997). After first describing the core components and the philosophical underpinnings of CQR, the authors examined how it has been applied in terms of the consensus process, biases, research teams, data collection, data analysis, and writing up the results and discussion sections of articles. On the basis of problems that have arisen in each of these areas, the authors made recommendations for modifications of the method. The authors concluded that CQR is a viable qualitative method and suggest several ideas for research on the method itself. (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)     

The analysis of profile data.

"Methods were suggested for handling 3 problems in the analysis of test profiles: measuring the similarity of profiles, discriminating the typical profiles of two or more groups, and clustering profiles into homogeneous groups. The suggested methods were, respectively, picturing profiles as interpoint distances in Euclidean space, use of the linear multiple-discriminant function, and factor analysis of profile cross-product terms. Some suggestions were given about transformations of profile data before further analysis." (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mixing qualitative and quantitative research in developmental science: Uses and methodological choices.

Multiple methods are vital to understanding development as a dynamic, transactional process. This article focuses on the ways in which quantitative and qualitative methodologies can be combined to enrich developmental science and the study of human development, focusing on the practical questions of when and how. Research situations that may be especially suited to mixing qualitative and quantitative approaches are described. The authors also discuss potential choices for using mixed quantitative- qualitative approaches in study design, sampling, construction of measures or interview protocols, collaborations, and data analysis relevant to developmental science. Finally, they discuss some common pitfalls that occur in mixing these methods and include suggestions for surmounting them. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The rise of qualitative research in psychology.

A study is presented on the rise of qualitative research in psychology over the 20th century. The incidence of qualitative research as indicated by several search terms (i.e., "qualitative research," "grounded theory," "discourse analy*," "empirical phenomenological," and "phenomenological psychology") was traced through the PsycINFO and Dissertation Abstracts International databases. It was found that, with the exception of the search terms having to do with phenomenology, records containing these search terms were basically non-existent until the 1980s, when there was a sharp rise that intensified in the 1990s. The PsycINFO records were sorted according to (1) whether they came from psychology or other social and health science disciplines; (2) region of origin; (3) the types of document to which they referred; and (4) whether they focused on the methodology or the application of qualitative research. A number of interesting differences emerged from this comparative analysis. Implications of the findings for the supposition that a paradigm shift may be underway are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to functional data analysis.

Psychologists and behavioural scientists are increasingly collecting data that are drawn from continuous underlying processes. We describe a set of quantitative methods, Functional Data Analysis (FDA), which can answer a number of questions that traditional statistical approaches cannot. These methods are applicable for analyzing many datasets that are common in experimental psychology, including time series data, repeated measures, and data distributed over time or space as in neuroimaging experiments. The primary advantage of FDA is that it allows the researcher to ask questions about when in a time series differences may exist between two or more sets of observations. We discuss functional correlations, principal components, the derivatives of functional curves, and analysis of variances models. (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)     

Qualitative data analysis: A compendium of techniques and a framework for selection for school psychology research and beyond.

Qualitative researchers in school psychology have a multitude of analyses available for data. The purpose of this article is to present several of the most common methods for analyzing qualitative data. Specifically, the authors describe the following 18 qualitative analysis techniques: method of constant comparison analysis, keywords-in-context, word count, classical content analysis, domain analysis, taxonomic analysis, componential analysis, conversation analysis, discourse analysis, secondary analysis, membership categorization analysis, narrative analysis, qualitative comparative analysis, semiotics, manifest content analysis, latent content analysis, text mining, and microinterlocutor analysis. Moreover, the authors present a new framework for organizing these analysis techniques via the four major sources of qualitative data collected: talk, observations, drawings/photographs/videos, and documents. As such, the authors hope that our compendium of analytical techniques should help qualitative researchers in school psychology and beyond make informed choices for their data analysis tools. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research.

Reviews Kline's book (see record 2004-13019-000) which reviews the controversy regarding significance testing, offers methods for effect size and confidence interval estimation, and suggests some alternative methodologies. Whether or not one accepts Kline's view of the future of statistical significance testing, there is much of value in this book. As a textbook, it could serve as a reference for an upper level undergraduate course but it would be more appropriate for a graduate course. The book is a thought-provoking examination of the uneasy alliance between null hypothesis significance testing, and effect size and confidence interval estimation. There is much in this book for those on both sides of the null hypothesis testing debate and for those unsure where they stand. Whatever the future holds, Kline has done well in illustrating recent advances to statistical decision-making. (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)     

The use of multiple imputation for the analysis of missing data.

This article provides a comprehensive review of multiple imputation (MI), a technique for analyzing data sets with missing values. Formally, MI is the process of replacing each missing data point with a set of m > 1 plausible values to generate m complete data sets. These complete data sets are then analyzed by standard statistical software, and the results combined, to give parameter estimates and standard errors that take into account the uncertainty due to the missing data values. This article introduces the idea behind MI, discusses the advantages of MI over existing techniques for addressing missing data, describes how to do MI for real problems, reviews the software available to implement MI, and discusses the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using "mark sense" for ratings and personal data collection.

The use of IBM "mark sense" cards for data collection, followed by standard machine handling of the punched cards, is described in connection with the analysis of hospital admissions data, correctional school data, ratings analysis, and similar applications. (PsycINFO Database Record (c) 2010 APA, all rights reserved)