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)     

Acquisition of categorical color perception: A perceptual learning approach to the linguistic relativity hypothesis.

Color perception can be categorical: Between-category discriminations are more accurate than equivalent within-category discrimination. The effects could be inherited, learned, or both. The authors provide evidence that supports the possibility of learned categorical perception (CP). Experiment 1 demonstrated that observers' color discrimination is flexible and improves through repeated practice. Experiment 2 demonstrated that category learning simulates effects of "natural" color categories on color discrimination. Experiment 3 investigated the time course of acquired CP. Experiment 4 found that CP effects are acquired through hue- and lightness-based category learning and obtained interesting data on the dimensional perception of color. The data are consistent with the possibility that language may shape color perception and suggest a plausible mechanism for the linguistic relativity hypothesis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An alternative to Marascuilo's "large-sample multiple comparisons" for proportions.

Comments on the original article by L. A. Marascuilo (see record 1966-07190-001) which discusses large-sample multiple comparisons. An alternative to Marascuilo's χ2 analogue of Scheffé's theorem for performing tests on multiple linear contrasts among proportions coming from K independent populations is described. Based on the arcsin transformation of p, the proposed alternative is suitable for small samples and is computationally simpler. Tests of complex contrasts are also exemplified. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An application of psychological scaling methods to content analysis: The use of empirically derived criterion weights to improve intercoder reliability.

A method is described in which a psychological scaling technique is applied to the analysis of the contents of written messages in order to provide a more precise metric for such measurement. The attribute to be measured was the extent to which each message communicated an attempt on the part of the writer to control the group's decision or procedures. 2 scales were developed, a logical scale comprised of 9 categories, and an empirical scale based on the application of Thurstone's successive interval technique to a set of written messages. The empirical scale was found to have a higher reliability than the logical scale with untrained coders. Possible reasons for the superiority of the empirical scale were discussed, and suggestions made concerning its use in future research. (16 ref.) (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)     

Transcendental interpretation: An alternative approach to psychological inquiry.

Presents the transcendental interpretation approach to psychological inquiry based on the use of the Kantian transcendental argument, which relies on the criterion of consistency. Kant's purpose in employing this argument was as a means for justifying particular knowledge claims. Use of this approach is illustrated by examining the intersubjective and moral conditions necessary for the practice of selfhood. It is argued that phenomena of interest to psychological study differ fundamentally from those of natural science, rendering some of the methods applied in the latter inappropriate to the study of the former. The transcendental argument can provide a direction for psychological research in 2 ways: By helping it to lend intelligibility to its subject matter, and extending ordinary practices by making explicit the goods and norms that motivate them. (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)     

Retranslation of expectations: An approach to the construction of unambiguous anchors for rating scales.

A procedure was tested for the construction of evaluative rating scales anchored by examples of expected behavior. Expectations, based on having observed similar behavior, were used to permit rating in a variety of situations without sacrifice of specificity. Examples, submitted by head nurses as illustrations of nurses' behavior related to a given dimension were retained only if reallocated to that dimension by other head nurses, and then scaled as to desirability. Agreement for a number of examples was high, and scale reliabilities ranged above .97. Similar content validity should be obtained in other rating situations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An approach to the validity of the Taylor Anxiety Scale: scores of medical and psychiatric patients.

The Taylor scale is administered individually to a clinic sample of medical and psychiatric outpatients with the result that significant differences in mean scores, distributions, and the point biserial r of .57 indicate that this questionnaire can distinguish a psychiatric population from a normal one with reasonable efficiency. Sampson and Bindra's suggestion that Taylor scores within a limited range (19-33) are more likely to be associated with a clinical diagnosis of "anxious" than scores outside this range, are not borne out. Both findings were confirmed in a cross-validation study utilizing hospitalized VA medical and psychiatric patients. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

What are the causes of reduced fit in scaling and clustering studies of semantic proximity data, and how else to measure them: A comment on Storms et al. (2003).

G. Storms, T. Dirikx, J. Saerens, S. Verstraeten, and P. P. De Deyn (2003) noted that the goodness-of-fit indices for scaling and clustering solutions of proximity data from populations with hypothesized semantic deficits tend to be reduced in comparison with control participants, and that this precludes an unambiguous interpretation of such data. However, this effect is not necessarily as inconsistent with the proposal of semantic degradation in these groups, or as consistent with explanations in terms of access or attentional problems, as is suggested by G. Storms et al. Also, although performance on word fluency tasks is certainly constrained by the ability to access stored information, there are ways in which this confounding influence may be controlled for in the study of semantic deficits. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Classification in context: An alternative approach to identifying early reading disability.

This study evaluated an alternative method of identifying early reading difficulty. L. S. Fuchs and D. Fuchs (1998) proposed that academic problems could be indexed by a dual discrepancy on level and slope of performance, relative to classmates, on curriculum-based measurement tasks. From a sample of 694 1st- and 2nd-grade children, we identified 47 children as dually discrepant in reading and compared them with 17 children identified as IQ-reading achievement discrepant and 28 children identified as low achieving. The dually discrepant children were younger and more impaired on phonological processes and teacher ratings of academic competence and social behaviors. This group also reflected the gender and racial distributions of the population. Single-point measures of fluency and phonological awareness were not sensitive indicators of reading problems, suggesting that ongoing assessment and evaluation may be necessary for valid identification. (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)     

Review of Psychotherapeutic change: An alternative approach to meaning and measurement.

Reviews the book, Psychotherapeutic change: An alternative approach to meaning and measurement by Alvin R. Mahrer (1985). This small and unpretentious volume is a welcome addition to the growing number of recent publications in the field of psychotherapy research that emphasize the need for innovative and more dynamic and functional conceptualizations of the meaning and measurement of psychotherapeutic change. Despite (or perhaps because of) its provocative content, Psychotherapeutic Change is an enjoyable book to read. Mahrer's style of writing is engaging, clear, and free of the customary clinical jargon. Also refreshing is the fact that the material throughout the book comes straight from the concerns of a practitioner. Although the reader may not always agree with the author and may be annoyed by the occasional repetitiveness of some ideas, this book will prove to be a source of stimulating ideas for students, clinicians, and researchers of psychotherapy alike. (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)     

Stability of nonlinear principal components analysis: An empirical study using the balanced bootstrap.

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate normality. For nonlinear PCA, however, standard options for establishing stability are not provided. The authors use the nonparametric bootstrap procedure to assess the stability of nonlinear PCA results, applied to empirical data. They use confidence intervals for the variable transformations and confidence ellipses for the eigenvalues, the component loadings, and the person scores. They discuss the balanced version of the bootstrap, bias estimation, and Procrustes rotation. To provide a benchmark, the same bootstrap procedure is applied to linear PCA on the same data. On the basis of the results, the authors advise using at least 1,000 bootstrap samples, using Procrustes rotation on the bootstrap results, examining the bootstrap distributions along with the confidence regions, and merging categories with small marginal frequencies to reduce the variance of the bootstrap results. (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)     

Using Profile Analysis via Multidimensional Scaling (PAMS) to identify core profiles from the WMS-III.

Profile Analysis via Multidimensional Scaling (PAMS) is a procedure for extracting latent core profiles in a multitest data set. The PAMS procedure offers several advantages compared with other profile analysis procedures. Most notably, PAMS estimates individual profile weights that reflect the degree to which an individual's observed profile approximates the shape and scatter of latent core profiles. The PAMS procedure was applied to index scores of nonreplicated participants from the standardization sample (N = 1,033) for the Wechsler Memory Scale-Third Edition (D. Tulsky, J. Zhu, & M. F. Ledbetter, 2002). PAMS extracted discrepant visual memory and auditory memory versus working memory core profiles for the complete 16- to 89-year-old sample and discrepant working memory and auditory memory versus working memory core profiles for the 75- to 89-year-old cohort. Implications for use of PAMS in future research are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Beyond constant comparison qualitative data analysis: Using NVivo.

The purposes of this paper are to outline seven types of qualitative data analysis techniques, to present step-by-step guidance for conducting these analyses via a computer-assisted qualitative data analysis software program (i.e., NVivo9), and to present screenshots of the data analysis process. Specifically, the following seven analyses are presented: constant comparison analysis, classical content analysis, keyword-in-context, word count, domain analysis, taxonomic analysis, and componential analysis. It is our hope that providing a clear step-by-step process for conducting these analyses with NVivo9 will assist school psychology researchers in increasing the rigor of their qualitative data analysis procedures. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data.

Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model, a growth mixture model, and a latent class growth analysis. They also describe a longitudinal latent class analysis, which requires fewer assumptions than the first 3. Finally, they illustrate all of the models using actual longitudinal adolescent alcohol-use data. They guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)