A general framework for the statistical analysis of sequential dyadic interaction data.

Recent interest in sequential dyadic interactions has motivated researchers to develop methods appropriate for the analysis of such data. After briefly reviewing a series of methodological papers focusing on the analysis of discrete-valued observations, we present a general framework for studying many substantive effects, including dominance and autodependencies, in social interactions measured on dyads. We show how this framework allows a researcher to study dyadic interactions measured at two or more time points on one or more relations. The methods described here are general enough to permit the simultaneous analysis of the sequential relational variables and attribute variables (such as sex of actors or emotional status of the dyad) recorded on either the dyad or the actors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The star plot: an alternative display method for multivariate data in the analysis of food and drugs

The star plot (SP) is a method of displaying multivariate data. It can be used to display data with more than two variables. Combined with principal component analysis (PCA), more than two PCs can be displayed in one plane. Different variants of this method are applied to an atomic absorption spectrometry (AAS) data set and to three near infra-red (NIR) spectral data sets. The results show that SP offers an easy way of visualising the multivariate data for food and drugs in a plane, and it is able to help the analyst to identify and to detect different qualities of food and drug composition. Moreover, when an object is added or removed, the PC's must be computed all over again, which is not the case for the SP-plot. The application of SP to the examples presented in the text suggests that the SP approach can be applied as an alternative method for displaying multivariate chemometric data in place of PCA or, to improve visualisation of the results already obtained with PCA.     

A model for triadic relations.

Psychologists have analyzed dyadic data with a social relations model (D. A. Kenny, 1994). This article develops an analogous model for triadic data. This triadic relations model, a 3-way random-effects analysis of variance, can estimate 7 variances and 16 covariances from a round-robin of 3-person interactions. This article applies this model to perceptions of liking among 72 college-student acquaintances. These variance-components methods could also be used to study cognitive balance, social networks, social perception, and group performance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Cluster Analysis in Family Psychology Research.

This article discusses the use of cluster analysis in family psychology research. It provides an overview of potential clustering methods, the steps involved in cluster analysis, hierarchical and nonhierarchical clustering methods, and validation and interpretation of cluster solutions. The article also reviews 5 uses of clustering in family psychology research: (a) deriving family types, (b) studying families over time, (c) as an interface between qualitative and quantitative methods, (d) as an alternative to multivariate interactions in linear models, and (e) as a data reduction technique for small samples. The article concludes with some cautions for using clustering in family psychology research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A general approach for estimating scale score reliability for panel survey data.

Scale score measures are ubiquitous in the psychological literature and can be used as both dependent and independent variables in data analysis. Poor reliability of scale score measures leads to inflated standard errors and/or biased estimates, particularly in multivariate analysis. Reliability estimation is usually an integral step to assess data quality in the analysis of scale score data. Cronbach’s α is a widely used indicator of reliability but, due to its rather strong assumptions, can be a poor estimator (L. J. Cronbach, 1951). For longitudinal data, an alternative approach is the simplex method; however, it too requires assumptions that may not hold in practice. One effective approach is an alternative estimator of reliability that relaxes the assumptions of both Cronbach’s α and the simplex estimator and thus generalizes both estimators. Using data from a large-scale panel survey, the benefits of the statistical properties of this estimator are investigated, and its use is illustrated and compared with the more traditional estimators of reliability. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The statistical analysis of dyadic social behavior: A multivariate approach.

Extends the logic of the univariate model for studying dyadic social interaction and presents a multivariate model. The more general multivariate model is illustrated using hypothetical data and real data from a study by M. Jacklin et al (see record 1979-25785-001) on girl–girl, boy–boy, and girl–boy interactions among 45 preschoolers. The 8 variables used included passive behavior, frequency of aggression, and frequency of social withdrawal. Some advantages of this model include detection of Type I error rates and generality. (10 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reconsidering history of psychology's borders.

By accepting psychology's borders of concern, some histories of psychology tend to mute significant interfaces between research and society. The alternative approach of critical theory provides histories more sensitive to these relations. Critical theory's conceptual problems with regress of explanation and with determining adequate criteria for evaluating differing historical narratives are considered. By using this approach, the study of history of psychology can broaden its borders to include more socially responsive and morally engaged issues. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Inference using complex data from surveys and experiments.

Examines methods for analyzing complex data (i.e., data that do not conform to the assumptions of independence and homoscedasticity on which many classical procedures are based). Primary attention is given to regression analysis, with ANOVA as a special case, though reference to related work on loglinear models and logit analysis is also made. The problems associated with using standard methods and software on complex data are discussed. Much of the work on alternative strategies for complex data analysis is based on an inferential framework that is fundamentally different from the model-based inference familiar to most psychologists. Though model-based inference is the most popular approach to analyzing experiments in psychology, the randomization approach is increasingly being advocated as an alternative. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Group sequential monitoring of distribution-free analyses of repeated measures

In many clinical trials the principal analysis consists of a 1 degree of freedom test based on an aggregate summary statistic for a set of repeated measures. Various methods have been proposed for the marginal analysis of such repeated measures that entail estimates of a measure of treatment group difference (the treatment effect) at each of K repeated measures and a consistent estimate of the covariance matrix, where asymptotically these estimates are normally distributed. One can then obtain an overall large sample 1-d.f. test of group differences, such as by taking the average of these K estimates. These methods include the Wei-Lachin family of multivariate rank tests and a corresponding multivariate analysis using the Mann-Whitney difference estimator as a measure of treatment group differences. Other methods, such as O'Brien's non-parametric test, are based on a single summary score for each patient, such as the within-patient mean value. These, and other such methods, allow for some observations to be missing at random. Herein I employ sequential data augmentation to conduct group sequential analyses using a 1 degree of freedom test from a multivariate Mann-Whitney analysis and for the O'Brien rank test. Su and Lachin used this method to perform group sequential analyses of a vector of Hodges-Lehmann estimators. By augmentating the data from the sequential looks in a single analysis, one obtains an estimate of the covariance of the estimates at each look, from which one obtains an estimate of the correlations among the sequential 1-d.f. test statistics. I describe a simple secant algorithm to determine the group sequential boundaries based on recursive integration of the standard multivariate normal distribution with the estimated correlation matrix. Although the boundary obtains readily using the method of Slud and Wei, the more flexible method of Lan and DeMets may be preferred. The true information fraction at each look, needed to apply the spending function method of Lan and DeMets, however, is unknown. Thus, I also describe the use of a surrogate measure of information.     

Evaluating the effectiveness of family therapies: An integrative review and analysis.

A number of previous reviews of studies evaluating the effectiveness of family therapy have varied in their conclusions. This review is the first attempt at an integrated statistical analysis of family therapy effectiveness research. Family therapy was found to have positive effects compared with both no-treatment and alternative treatment controls, as measured by family interactions and behavior ratings. Follow-up data demonstrated that family therapy continued to show positive effects over time, but the effects were diminished and more variable than at posttreatment assessment. Future research should concentrate on comparative outcome studies with specific populations and with both legitimate alternative treatments and placebo controls. In addition, researchers need to describe in greater detail the family therapy procedures that are used. Outcome measures should represent multiple vantage points and should include measures of family interactions, preferably using both self-report and observational methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A nuclear-encoded function essential for translation of the chloroplast psaB mRNA in chlamydomonas

This paper demonstrates an alternative approach to studying the process of social support in close relationships by examining three tape-recorded conversations between breast cancer patients and their partners. We argue that existing research fails to take account of the "social" aspects of social support, and that new methods, sensitive to the complexities of human interaction, are needed. One promising method, tape-assisted recall (in which an audiotape of an interaction is played back to participants), allows researchers to examine the help-intended communication within a dyad as well as each participant's view of that communication. The conversations of the three couples and the participants' moment-by-moment perceptions of these conversations illustrate the complexities of help-intended communication. The commentaries in the tape-assisted recall sessions yielded insights into the interactions that were not always apparent on the surface: the personal meanings of the interactions, in the context of the couple's relationship, were necessary for understanding how support attempts are delivered effectively and why they sometimes fail. The strengths and limitations of the method for the study of social support are discussed. Implications for community health interventions to optimize support for couples coping with illness are also addressed.     

Multivariate approach to quality control in clinical chemistry

When monitoring analyzer performance in the clinical setting, laboratories are required to test multiple concentrations of control material on a daily basis. Because of the nature of laboratory testing, there is the potential for correlation between the concentrations of control material being monitored. Although traditional clinical quality-control approaches make an underlying assumption of independence with respect to the control concentrations, this will not always be the case. The presence of correlation in some circumstances suggests the use of a new approach for evaluating clinical laboratory monitoring data: the multivariate control chart. Such a chart (the chi2 chart) is evaluated and compared with traditional quality-control approaches used in the laboratory setting. Results indicate that the multivariate approach provides an attractive alternative to many traditional methods of quality assurance when control concentrations are correlated.     

Performance Evaluation of Reference Evapotranspiration Equations across a Range of Indian Climates

Reference crop evapotranspiration (ET0) is a key variable in procedures established for estimation of evapotranspiration rates of agricultural crops. In recent years, there is growing evidence to show that the more physically based FAO-56 Penman–Monteith (PM) combination method yields consistently more accurate ET0 estimates across a wide range of climates and is being proposed as the sole method for ET0 computations. However, other methods continue to remain popular among Indian practitioners either because of traditional usage or because of their simpler input data requirements. In this study, we evaluated the performances of several ET0 methods in the major climate regimes of India with a view to quantify differences in ET0 estimates as influenced by climatic conditions and also to identify methods that yield results closest to the FAO-56 PM method. Performances of seven ET0 methods, representing temperature-based, radiation-based, pan evaporation-based, and combination-type equations, were compared with the FAO-56 PM method using historical climate data from four stations located one each in arid (Jodhpur), semiarid (Hyderabad), subhumid (Bangalore), and humid (Pattambi) climates of India. For each location, ET0 estimates by all the methods for assumed hypothetical grass reference crop were statistically compared using daily climate records extending over periods of 3–4 years. Comparisons were performed for daily and monthly computational time steps. Overall results while providing information on variations in FAO-56 PM ET0 values across climates also indicated climate-specific differences in ET0 estimates obtained by the various methods. Among the ET0 methods evaluated, the FAO-56 Hargreaves (temperature-based) method yielded ET0 estimates closest to the FAO-56 PM method both for daily and monthly time steps, in all climates except the humid one where the Turc (radiation-based) was best. Considering daily comparisons, the associated minimum standard errors of estimate (SEE) were 1.35, 0.78, 0.67, and 0.31 mm/day, for the arid, semiarid, subhumid, and humid locations, respectively. For monthly comparisons, minimum SEE values were smaller at 0.95, 0.59, 0.38, and 0.20 mm/day for arid, semiarid, subhumid, and humid locations, respectively. These results indicate that the choice of an alternative simpler equation in a particular climate on the basis of SEE is dictated by the time step adopted and also it appears that the simpler equations yield much smaller errors when monthly computations are made. In order to provide simple ET0 estimation tools for practitioners, linear regression equations for preferred FAO-56 PM ET0 estimates in terms of ET0 estimates by the simpler methods were developed and validated for each climate. A novel attempt was made to investigate the reasons for the climate-dependent success of the simpler alternative ET0 equations using multivariate factor analysis techniques. For each climate, datasets comprising FAO-56 PM ET0 estimates and the climatic variables were subject to factor analysis and the resulting rotated factor loadings were used to interpret the relative importance of climatic variables in explaining the observed variabilities in ET0 estimates. Results of factor analysis more or less conformed the results of the statistical comparisons and provided a statistical justification for the ranking of alternative methods based on performance indices. Factor analysis also indicated that windspeed appears to be an important variable in the arid climate, whereas sunshine hours appear to be more dominant in subhumid and humid climates. Temperature related variables appear to be the most crucial inputs required to obtain ET0 estimates comparable to those from the FAO-56 PM method across all the climates considered.     

Scientific versus statistical inference.

We argue that the goals of scientists in data analysis and scientific communication do not match the logic of hypothesis testing as it is typically taught in introductory statistics courses. The "Intro Stats" method has a number of well-known logical fallacies and the results of the procedures are often misinterpreted. However, we suspect that these problems do not directly compromise researchers' inferences concerning their data because researchers implicitly use a different method for identifying the implications of data. In particular, we argue that researchers are typically interested in comparing the relative adequacy of different accounts or theoretical explanations rather than rejecting an artificially constructed null hypothesis; a simple analysis of recent journal articles supports this view. An alternative approach to data analysis and scientific communication is to present the strength of the evidence provided by an experiment in the form of a maximum likelihood ratio. This approach is more consistent with the logic of comparing alternative accounts that scientists commonly use. Likelihood ratios are simple to calculate and provide a simple and intuitive summary of the results relevant to evaluating the alternative explanations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

RNA polymerase III interferes with Ty3 integration

Many cytological processes such as cell proliferation, differentiation, transformation, apoptosis, etc., are accompanied by specific chromatin changes, usually identified on the basis of the relative content of euchromatin and heterochromatin. In order to achieve a quantitative, non-subjective evaluation of the chromatin pattern, two different approaches may be undertaken, one consisting in the analysis of the several morphological features of chromatin grains (size, shape, density, arrangement, and distribution), and the second consisting in the analysis of the chromatin globally considered as a coherent texture. Although the second approach appears to be simpler and more suitable, methods of texture analysis--including those specifically designed for the analysis of the chromatin pattern--are rarely applied due mainly to the unsuitability of sampling procedures and the excessive crypticism of results. As an alternative to traditional texture analysis, we suggest a method supported by a sound mathematical theory and approximately 30 years of applications in the field of geostatistics. The method, called variogram, analyzes the intrinsic structure of data sampled at different distance intervals and directions, and outputs easily understandable results. Recently, variogram analysis has successfully been exported from geostatistics to other fields (for example, ecology and epidemiology) that make use of spatially referenced variables. Based on the fact that pixels represent a perfect array of data ordered at regular distance intervals and directions, the variogram can be adopted to explore nuclear images and recognize chromatin patterns. Variograms of different nuclei can be summarized by multivariate methods without the need of previous standardization of data. This allows comparison and discrimination of chromatin patterns from mixed cell populations. Preliminary data obtained from young neurons undergoing massive apoptosis reveal a self-consistent map of nuclear changes correlated to the degenerative process.     

Increasing the impact of cluster analysis research: The case of psychiatric classification.

The publication of the DSM-III by the American Psychiatric Association has fueled interest among psychologists in developing alternative classifications. A popular approach is to use multivariate methods known as cluster analysis to identify clinical syndromes. Empirical clustering techniques offer potential for making diagnoses more objective, reliable, and predictive of outcome from differential treatment. However, problems exist at the interface between this statistical technology and psychiatric classification. To date, cluster-analytic studies have had limited impact on psychiatric nomenclature, and clinicians have not found the cluster-derived syndromes to be relevant to everyday practice. The present authors discuss reasons that underlie the lack of impact and describe directions for upgrading both the scientific quality and clinical acceptance of cluster-analysis research. It is concluded that more emphasis must be placed on fostering the clinical use of empirically derived classifications. (69 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Truncated recurrent event survival models for methadone data

Truncated multivariate survival models are proposed for the analysis of data from the South Australian Methadone Program. Injecting drug users may have several entries to and exists from the program. A marginal approach for recurrent events is found to provide a useful and simplified basis for modeling the retention of injecting drug users on methadone. The likelihoods and results of fitting two forms of truncated survival models are presented, together with recommendations for statistical analysis using S-PLUS. The relative merits of the marginal approach in this context and alternative ways of viewing the data are discussed.     

Have confidence in contact.

In an article in the May–June 2009 American Psychologist, we discussed a new approach to reducing prejudice and encouraging more positive intergroup relations (Crisp & Turner, 2009). We named the approach imagined intergroup contact and defined it as “the mental simulation of a social interaction with a member or members of an outgroup category” (Crisp & Turner, 2009, p. 234). Our proposition is that simply imagining contact with outgroup members can produce more positive perceptions of outgroups. In his commentary, Honeycutt noted that our “article is excellent in its premise” (p. 129), but he was critical of our decision not to discuss his own work in imagined interactions (IIs). Imagined contact is not a magic cure, and it is not a one-shot solution to the problem of prejudice. But as a first step on the road to reduced prejudice and more positive intergroup relations, it may just turn out to be invaluable. (PsycINFO Database Record (c) 2010 APA, all rights reserved)