Classroom application of a trial-based functional analysis

We evaluated a trial-based approach to conducting functional analyses in classroom settings. Ten students referred for problem behavior were exposed to a series of assessment trials, which were interspersed among classroom activities throughout the day. Results of these trial-based functional analyses were compared to those of more traditional functional analyses. Outcomes of both assessments showed correspondence in 6 of the 10 cases and partial correspondence in a 7th case. Results of the standard functional analysis suggested reasons for obtained differences in 2 cases of noncorrespondence, which were verified when portions of the trial-based functional analyses were modified and repeated. These results indicate that a trial-based functional analysis may be a viable assessment method when resources needed to conduct a standard functional analysis are unavailable. Implications for classroom-based assessment methodologies and future directions for research are discussed.     

Performing high‐powered studies efficiently with sequential analyses

Running studies with high statistical power, while effect size estimates in psychology are often inaccurate, leads to a practical challenge when designing an experiment. This challenge can be addressed by performing sequential analyses while the data collection is still in progress. At an interim analysis, data collection can be stopped whenever the results are convincing enough to conclude that an effect is present, more data can be collected, or the study can be terminated whenever it is extremely unlikely that the predicted effect will be observed if data collection would be continued. Such interim analyses can be performed while controlling the Type 1 error rate. Sequential analyses can greatly improve the efficiency with which data are collected. Additional flexibility is provided by adaptive designs where sample sizes are increased on the basis of the observed effect size. The need for pre‐registration, ways to prevent experimenter bias, and a comparison between Bayesian approaches and null‐hypothesis significance testing (NHST) are discussed. Sequential analyses, which are widely used in large‐scale medical trials, provide an efficient way to perform high‐powered informative experiments. I hope this introduction will provide a practical primer that allows researchers to incorporate sequential analyses in their research. Copyright © 2014 John Wiley & Sons, Ltd.     

Causal priority and causal conditionship

Temporal analyses of causal directionality fail if causes needn't precede their effects. Certain well-known difficulties with alternative (non-temporal) analyses have, in recent accounts, been avoided by attending more carefully to the formal features of relations typically figuring in philosophical discussions of causation. I discuss here a representative of such accounts, offered by David Sanford, according to which a correct analysis of causal priority must issue from viewing the condition relation as nonsymmetrical. The theory is shown first to be an implicitly counterfactual treatment at its base: this provides for an explicit reformulation of several key notions in the theory. An argument is then presented, independent of these modal considerations, for the conclusion that causal priority is possible only given certain implausible assumptions about the asymmetric character of causal laws which, I claim, are not met. The best objection to this argument is shown to fail on several counts, partly in light of the counterfactual results offered earlier. It is concluded that, if laws are symmetric, analyses of the sort discussed must look elsewhere for the source of causal priority; but if laws are not symmetric, resting causal priority so heavily on nomological asymmetry is no analysis at all.     

Mastering the Art of Chain Analysis in Dialectical Behavior Therapy

Behavioral chain analyses, which are common in behavioral and cognitive-behavioral treatments more broadly, are the primary assessment tool in Dialectical Behavior Therapy (DBT). As such, they are conducted in nearly every DBT session. Despite their importance in the treatment, very little has been written about the “how-tos” of chain analyses in DBT or how to improve therapist skill in this area. This article describes the rationale and proposed functions of chain analyses, provides details about the various components of the chain analysis, and includes common problems that arise in conducting chain analyses. A case example of a detailed chain analysis is provided.     

Life-style and substance use among male African American urban adolescents: A cluster analytic approach

Cluster analyzed four variables: school attendance, employment, church attendance, and delinquency, to develop life-style profiles. Data from 218 African-American urban adolescents were used in the study. Five meaningful clusters were retained and subjected to criterion validity analyses using measures of spirituality, participation in a voluntary organization, self-esteem, and friend's substance use. The five clusters were then compared on cigarette, alcohol, marijuana, and hard drug use. The results suggest that a life-style that includes an adaptive compensatory behavior component may be more adaptive than a life-style that does not include compensatory behavior. For example, youths who left high school before graduation but were involved in church reported less alcohol and substance use than youths who left school and were not involved in any meaningful instrumental activity. Implications for intervention and future research on high-risk behaviors are discussed.     

Controlling Correlational Bias via Confirmatory Factor Analysis of MTMM Data

Confirmatory factor analysis of multitrait-multimethod (MTMM) data has proven to be a useful tool for assessing convergent and discriminant validity. However, researchers have not made full use of the results of MTMM analyses in examining the relationship between MTMM factors and variables outside the MTMM. Often, researchers simply average the various measures of each trait. Alternatively, they estimate LISREL MTMM models, but estimate only relationships between MTMM traits and the outside variables. In the present article, we show that these two approaches to analyzing data outside the MTMM produce equally highly biased parameter estimates when the actual correlations between MTMM method factors and the outside variables are substantial. An algebraic explanation and a simulated data illustration are given for the bias due to misspecification. Also, the problem is illustrated with a brief empirical example. Implications for applied research are discussed.     

A Bayesian Power Analysis Procedure Considering Uncertainty in Effect Size Estimates from a Meta-analysis

In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use observed effect sizes and prior distributions to obtain the posterior distribution of the effect size and model parameters. Then, we simulate effect sizes from the obtained posterior distribution. For each simulated effect size, we obtain a power value. With an estimated power distribution for a given sample size, we can estimate the probability of reaching a power level or higher and the expected power. With a range of planned sample sizes, we can generate a power assurance curve. Both the conventional frequentist and our Bayesian procedures were applied to conduct prospective power analyses for two meta-analysis examples (testing standardized mean differences in example 1 and Pearson's correlations in example 2). The advantages of our proposed procedure are demonstrated and discussed.     

The equivalence of regression models using difference scores and models using separate scores for each informant: implications for the study of informant discrepancies

Research on informant discrepancies has increasingly utilized difference scores. This article demonstrates the statistical equivalence of regression models using difference scores (raw or standardized) and regression models using separate scores for each informant to show that interpretations should be consistent with both models. First, regression equations were used to demonstrate that difference score models are equivalent to models using separate scores for each informant. Second, a hypothesis-driven empirical example (218 mother-child dyads, mean age = 11.5 years, 49% female participants, 49% White, 47% African American) was used to provide an illustration of the equivalence of the 2 models. Implications of the equivalence of models using difference scores and models using separate scores for each informant are discussed in terms of the growing prevalence of an interpretation in the literature of difference score analyses that is inconsistent with results from equivalent separate informant analyses. Differences in the separate predictive ability of informants should be acknowledged as an alternative interpretation of the difference score regression coefficient.     

FEMINIST POSTSTRUCTURALISM AND DISCOURSE ANALYSIS

In this article I suggest that feminist poststructuralism (Weedon, 1987) is of great potential value to feminist psychologists seeking more satisfactory ways of theorizing gender and subjectivity. Some key elements of this theoretical perspective are discussed, including an understanding of knowledge as socially produced and inherently unstable, an emphasis on the importance of language and discourse, and a decentering of the subject. Discourse analysis is discussed as one way of working that is consistent with feminist poststructuralist theory. To illustrate this approach, an example is presented from my work on the sexual coercion of women within heterosexual relationships.     

Videotaping effects on the behaviors of low income mothers and their infants during floor-play interactions

Interaction behaviors and language measures of low income mothers and their 12-month-old infants were compared for floor-play situations in which the mother was aware and unaware of being videotaped. When the mothers were aware of being videotaped, they were more proximal to their infants, offered and demonstrated toys more frequently, engaged in more frequent interaction games, vocalized more frequently, emitted a greater number of words as well as declarative and imperative utterances, and their infants engaged in more constructive play. Combining the analysis of variance and correlational analyses results suggested that the verbal behaviors of mothers were inflated and their non-verbal behaviors were distorted when they were aware of being videotaped. The implications of these data for the use of videotaping as an assessment and intervention tool are discussed.     

The Recoverability of P-technique Factor Analysis

It seems that just when we are about to lay P–technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables—dynamic factor models—it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even though it does not explicitly model lagged information, P–technique's ability to recover the parameters of underlying dynamic processes involving lagged relations among the manifest variables is apparently robust and accurate. An empirical example is presented using 103 days of affective mood self-ratings from a young pregnant woman. Implications of the simulation and empirical findings are briefly discussed.     

Conditions for rasch-dichotomizability of the unidimensional polytomous rasch model

Jansen and Roskam (1986) discussed the compatibility of the unidimensional polytomous Rasch model with dichotomization of the response continuum. They derived a rather strict condition in which dichotomization of multicategory data that fit the unidimensional polytomous Rasch model, results in dichotomous data which fit the dichotomous Research model with effectively the same subject parameter. In this paper a more general dichotomization condition is derived for the polytomous Rasch model, which appears less restrictive, but upholds that the intrinsic logic of the unidimensional polytomous Rasch model defies dichotomization in general. The robustness of dichotomous analysis investigated in a simulation study. It shows a close relation with the two-parameters (Birnbaum) model. Theoretical and methodological implications are discussed.The authors are indebted to H. Müller (personal communication, August 1986), for giving an example which pointed toward the core equation in this paper. The authors also acknowledge the critical comments of Th. Bezambinder and P. Wakker, and of Psychometrika's reviewers to an earlier version of this paper.     

Structural equation modeling: reviewing the basics and moving forward

This tutorial begins with an overview of structural equation modeling (SEM) that includes the purpose and goals of the statistical analysis as well as terminology unique to this technique. I will focus on confirmatory factor analysis (CFA), a special type of SEM. After a general introduction, CFA is differentiated from exploratory factor analysis (EFA), and the advantages of CFA techniques are discussed. Following a brief overview, the process of modeling will be discussed and illustrated with an example using data from a HIV risk behavior evaluation of homeless adults (Stein & Nyamathi, 2000). Techniques for analysis of nonnormally distributed data as well as strategies for model modification are shown. The empirical example examines the structure of drug and alcohol use problem scales. Although these scales are not specific personality constructs, the concepts illustrated in this article directly correspond to those found when analyzing personality scales and inventories. Computer program syntax and output for the empirical example from a popular SEM program (EQS 6.1; Bentler, 2001) are included.     

Polynomial Regression with Response Surface Analysis: A Powerful Approach for Examining Moderation and Overcoming Limitations of Difference Scores

Polynomial regression with response surface analysis is a sophisticated statistical approach that has become increasingly popular in multisource feedback research (e.g., self-observer rating discrepancy). The approach allows researchers to examine the extent to which combinations of two predictor variables relate to an outcome variable, particularly in the case when the discrepancy (difference) between the two predictor variables is a central consideration. We believe this approach has potential for application to a wide variety of research questions. To enhance interest and use of this technique, we provide ideas for future research directions that might benefit from the application of this analytic tool. We also walk through a step-by-step example of how to conduct polynomial regression and response surface analysis and provide all the tools you will need to do the analyses and graph the results (including SPSS syntax, formulas, and a downloadable Excel spreadsheet). Our example involves how discrepancies in perceived supervisor and organizational support relate to affective commitment. Finally, we discuss how this approach is a better, more informative alternative to difference scores and can be applied to the examination of two-way interactions in moderated regression.     

Nonlinear Bayesian analysis for single case designs

Several authors have suggested the use of multilevel models for the analysis of data from single case designs. Multilevel models are a logical approach to analyzing such data, and deal well with the possible different time points and treatment phases for different subjects. However, they are limited in several ways that are addressed by Bayesian methods. For small samples Bayesian methods fully take into account uncertainty in random effects when estimating fixed effects; the computational methods now in use can fit complex models that represent accurately the behavior being modeled; groups of parameters can be more accurately estimated with shrinkage methods; prior information can be included; and interpretation is more straightforward. The computer programs for Bayesian analysis allow many (nonstandard) nonlinear models to be fit; an example using floor and ceiling effects is discussed here.     

Decompositions and biplots in three-way correspondence analysis

In this paper correspondence analysis for three-way contingency tables is presented using three-way generalisations of the singular value decomposition. It is shown that in combination with Lancaster's (1951) additive decomposition of interactions in three-way tables, a detailed analysis is possible of the deviations from independence. Finally, biplots are shown to produce powerful graphical representations of the results from three-way correspondence analyses. An example from child development is used to illustrate the theoretical developments.     

EMPIRICALLY DERIVED JOB FAMILIES AS A FOUNDATION FOR THE STUDY OF VALIDITY GENERALIZATION

The purpose and background of a series of three studies dealing with validity generalization are discussed, an overview of the series is provided, and the initial study is described.
In the initial study a total of 76 insurance company jobs was analyzed by 203 raters in an effort to assess the potential usefulness of the Position Analysis Questionnaire (PAQ) as a job analysis device to be employed in a more extensive, companywide research program. Examination of rate-rerate and interrater reliability suggested that rate-rerate reliability was quite good, while interrater reliability estimates were somewhat lower. Cluster analysis techniques were applied to the five Overall and 17 Component dimensions of the PAQ yielding six job families in both analyses. The job families were described in terms of the various PAQ dimensions and were judged to be organizationally meaningful. Although the job clustering results were generally acceptable, it was conjectured that job families formed on the basis of company specific dimensions might prove even more meaningful.     

The Square Circle

This article shows that there are square circles (or equivalently, round squares) in the sense that there are mathematical objects that are at the same time both perfectly circular and perfectly square. The philosophical significance of this is discussed, especially in view of philosophy's widespread use of “square circle” as a typical example of an impossibility. In particular, the focus is on what the existence of square circles means for the possibility of conceptual analysis, and more generally what we can learn about the nature of non‐formal concepts and their use in philosophy.