Distribution-free tests of stochastic dominance for small samples

One variable is said to “stochastically dominate” another if the probability of observations smaller than x is greater for one variable than the other, for all x. Inferring stochastic dominance from data samples is important for many applications of econometrics and experimental psychology, but little is known about the performance of existing inferential methods. Through simulation, we show that three of the most widely used inferential methods are inadequate for use in small samples of the size commonly encountered in many applications (up to 400 observations from each distribution). We develop two new inferential methods that perform very well in a limited, but practically important, case where the two variables are guaranteed not to be equal in distribution. We also show that extensions of these new methods, and an improved version of an existing method, perform quite well in the original, unlimited case.     

A Test of Order-Constrained Hypotheses for Circular Data With Applications to Human Movement Science

Researchers studying the movements of the human body often encounter data measured in angles (e.g., angular displacements of joints). The evaluation of these circular data requires special statistical methods. The authors introduce a new test for the analysis of order-constrained hypotheses for circular data. Through this test, researchers can evaluate their expectations regarding the outcome of an experiment directly by representing their ideas in the form of a hypothesis containing inequality constraints. The resulting data analysis is generally more powerful than one using standard null hypothesis testing. Two examples of circular data from human movement science are presented to illustrate the use of the test. Results from a simulation study show that the test performs well.     

Strategy instruction for writing composition at school and at home

Abstract

The objectives of this study are 1) to analyze the effectiveness of a writing composition intervention program aimed to increase children's writing performance and other personal variables; 2) to analyze whether implementation in the environment (school and home) or responsible figures (parents and teachers) produced different effects in program effectiveness; and 3) to check whether increasing parental involvement in doing homework was sufficient to improve children's outcomes or whether prior family training is required to enable them to be efficient. The sample consisted of 112 students (5th-6th grade) and their families. 26 of them made up the EFP group (mothers implementing a writing program), 25 of them were assigned to the PAD group (mothers increasing help with writing homework), 35 to the PRO group (teachers applying a writing program) and 26 to the OC group (ordinary curriculum). We analyze the results and present and discuss our conclusions.     

Administering Quantitative Instruments With Qualitative Interviews: A Mixed Research Approach

The authors demonstrate how collecting quantitative data via psychometrically sound quantitative instruments during the qualitative interview process enhances interpretations by helping researchers better contextualize qualitative findings, specifically through qualitative dominant crossover mixed analyses. They provide an example of this strategy, whereby a baseline was established using a quantitative scale and normative data to help interpret qualitative interviews, resulting in what they call a mixed methods interview. Philosophical and practical implications are discussed.     

Data Collection in a Flat World: The Strengths and Weaknesses of Mechanical Turk Samples

Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this article, we review recent research about MTurk and compare MTurk participants with community and student samples on a set of personality dimensions and classic decision‐making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample's attitudes but similar to students' attitudes. Finally, MTurk participants are less extraverted and have lower self‐esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision‐making biases: they are present biased, risk‐averse for gains, risk‐seeking for losses, show delay/expedite asymmetries, and show the certainty effect—with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection and recommend that researchers using MTurk (1) include screening questions that gauge attention and language comprehension; (2) avoid questions with factual answers; and (3) consider how individual differences in financial and social domains may influence results. Copyright © 2012 John Wiley & Sons, Ltd.     

Trait anxiety and trait anger measured by ecological momentary assessment and their correspondence with traditional trait questionnaires

Ecological momentary assessments (EMA) of anxiety and anger/hostility were obtained every 25–30 min over two 24-h periods, separated by a median of 6 months, from 165 employees at a university in the Northeast. We used a multilevel trait-state-error structural equation model to estimate: (1) the proportion of variance in EMA anxiety and anger/hostility attributable to stable trait-like individual differences; (2) the correspondence between these trait-like components of EMA anxiety and anger/hostility and traditional questionnaire measures of each construct; and (3) the test–retest correlation between two 24-h averages obtained several months apart. After adjustment for measurement error, more than half the total variance in EMA reports of anxiety and anger/hostility is attributable to stable trait-like individual differences; however, the trait-like component of each construct is only modestly correlated with questionnaire measures of that construct. The 6-month “test–retest” correlations of latent variables representing the true 24-h EMA average anxiety and average anger are quite high (r ? 0.83). This study represents the longest follow-up period over which EMA-based estimates of traits have been examined. The results suggest that although the trait component (individual differences) of EMA momentary ratings of anxiety and anger is larger than the state component, traditional self-report questionnaires of trait anxiety and anger correspond only weakly with EMA-defined traits.     

Psychobiography in psychology: Past,present, and future

The present article reviews the history and emerging trends in the field of psychobiography. Five historical periods are highlighted: hagiography and pre-19th century study of lives; Freud and applied psychoanalysis; psychodynamic extensions and personology; modern multi-theoretical models; and psychobiography as interdisciplinary science. The author advocates for a science of psychobiography manifested in expanded and empirically validated theoretical models anchoring research. Further, attention is drawn to the need for more rigorous historiographic research methods weighting first-person sources and incorporating mixed methods designs. Increased attention to ethical and legal issues in the conduct and reporting of psychobiographical studies is also highlighted. Finally, emerging trends in psychobiography related to research production, academic training, and organisational initiatives are presented.     

Multicriteria Classification with Unknown Categories: A Clustering–Sorting Approach and an Application to Conflict Management

This work proposes an approach to cluster and sort a set of alternatives considering multi‐criteria categories with a partial order structure. It can be considered a heuristic approach because it does not attempt to derive an optimal partial order among all conceivable clusters of alternatives. Rather than this, it intends to be a simple approach that is transparent to the Decision Maker (DM) whose assistance is sought to help shaping the results. The approach proposed arises from the conjugation of traditional Clustering analysis and Multi‐criteria sorting tools. At the outset, the number of categories and their characteristics is unknown. First, we need to detect only the clusters themselves on the basis of a similarity measure independent of the preferences of the DM. Next, we detect potential partial order relations that might exist between them, according to the subjective preferences of the DM. Such preferences are elicited only after the DM has examined the clusters detected and deemed that these categories made sense. The new approach performs very well in a real‐world problem of management of intragroup conflicts and conflict handling strategies. Copyright © 2012 John Wiley & Sons, Ltd.     

Heterogeneous factor analysis models: A bayesian approach

Multilevel factor analysis models are widely used in the social sciences to account for heterogeneity in mean structures. In this paper we extend previous work on multilevel models to account for general forms of heterogeneity in confirmatory factor analysis models. We specify various models of mean and covariance heterogeneity in confirmatory factor analysis and develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference, model checking, and model comparison.We test our methodology using synthetic data and data from a consumption emotion study. The results from synthetic data show that our Bayesian model perform well in recovering the true parameters and selecting the appropriate model. More importantly, the results clearly illustrate the consequences of ignoring heterogeneity. Specifically, we find that ignoring heterogeneity can lead to sign reversals of the factor covariances, inflation of factor variances and underappreciation of uncertainty in parameter estimates. The results from the emotion study show that subjects vary both in means and covariances. Thus traditional psychometric methods cannot fully capture the heterogeneity in our data.     

Virtual reality and the new psychophysics

Virtual reality (VR) promises methodological rigour with the extra benefit of allowing us to study the context-dependent behaviour of individuals in their natural environment. Pan and Hamilton (2018, Br. J. Psychol.) provide a useful overview of methodological recommendations for using VR. Here, we highlight some other aspects of the use of VR. Our first argument is that VR can be useful by virtue of its differences from the normal perceptual environment. That is, by virtue of its relative non-realism and poverty of its perceptual elements, it can actually offer increased clarity with respect to the features of interest for the researcher. Our second argument is that VR exerts its measurable influence more by eliciting an acceptance of the virtual world (i.e., ‘suspension of disbelief’) rather than by eliciting a true belief of the realism of the VR environment. We conclude by providing a novel suggestion for combining neuroimaging methods with embodied VR that relies on the suspension of disbelief.