Using a sharp instrument to parse apart strategy and consistency: an evaluation of PPT and its assumptions

Potential Performance Theory (PPT) is a general theory for parsing observed performance into the underlying strategy and the consistency with which it is used. Although empirical research has supported that PPT is useful, it is desirable to have more information about the bias and standard errors of PPT findings. It also is beneficial to know the effects of violations of PPT assumptions. The authors present computer simulations that evaluate bias and standard errors at varying levels of strategy, consistency, and number of trials per participant. The simulations show that, when the assumptions are true, there is very little bias and the standard errors are low when there are moderate or large numbers of trials per participant (e.g., N=50 or N=100). But when the independence assumption is violated, PPT provides biased findings, although the bias is quite small unless the violations are large.     

Combination Scores for the Humm-Wads-Worth Temperament Scale

In an attempt to assess the degree to which specific stimulus-response associations are gradually acquired in learning a serial list, the order of the middle items was altered during acquisition. Five groups with 16 Ss per group had either no items switched, two items switched after four or eight test trials, or four items switched after four or eight test trials. The nonsense syllables were presented with slide projectors by means of standard serial anticipation procedures. Contrary to hypotheses, there were no overall differences between the four experimental groups and the control in trials to criterion or in total errors. However, although few experimental Ss reported noticing the switch, they made more errors on the trials immediately following the switch in comparison with the control group. These results are interpreted as disconfirming continuous, stimulus-specific association assumptions and supporting noncontinuous, nonassociative approaches.     

Finite Mixtures for Simultaneously Modeling Differential Effects and Nonnormal Distributions

Regression mixture models have been increasingly applied in the social and behavioral sciences as a method for identifying differential effects of predictors on outcomes. Although the typical specification of this approach is sensitive to violations of distributional assumptions, alternative methods for capturing the number of differential effects have been shown to be robust. Yet, there is still a need to better describe differential effects that exist when using regression mixture models. This study tests a new approach that uses sets of classes (called differential effects sets) to simultaneously model differential effects and account for nonnormal error distributions. Monte Carlo simulations are used to examine the performance of the approach. The number of classes needed to represent departures from normality is shown to be dependent on the degree of skew. The use of differential effects sets reduced bias in parameter estimates. Applied analyses demonstrated the implementation of the approach for describing differential effects of parental health problems on adolescent body mass index using differential effects sets approach. Findings support the usefulness of the approach, which overcomes the limitations of previous approaches for handling nonnormal errors.     

Temporal dynamics in attention bias: effects of sex differences,task timing parameters,and stimulus valence

New methods of calculating indices from the dot-probe task measure temporal dynamics in attention bias or fluctuations in attention bias towards and away from emotional stimuli over time. However, it is unclear how task-specific parameters such as stimulus valence and stimulus onset asynchrony (SOA) as well as participant sex affect temporal dynamics in attention bias. To address this, male and female participants (N?=?106) completed either a fearful or happy face dot-probe task with four levels of SOA (84?ms, 168?ms, 336?ms, and 672?ms). Results suggest that temporal dynamics in attention bias are (1) greatest at the longest SOA, (2) greater in females, and (3) insensitive to stimulus valence. These findings indicate that participant sex and task timing, but not stimulus valence, are related to temporal dynamics in attention bias and should be considered in future studies utilizing this approach.     

Diverging diamond interchanges: A driving simulator study

This study assessed driver performance while navigating a Diverging Diamond Interchange (DDI) compared to a standard intersection in a driving simulator. A total of 201 Western Australian (WA) drivers aged 18–80 years completed the simulator drive and questionnaire. Measures of driving simulator performance assessed included time spent out of lane, number of lane excursions, compliance to the speed limit, crashes and near misses. Other driving measures, which were recorded by the researcher, included driver errors/violations such as red-light violations, wrong way violations and navigation errors. Qualitative information was also obtained in a post exit interview with each participant regarding the difficulties they experienced when driving through the DDI. A repeated-measure analysis of variance (r-ANOVA) was undertaken to examine differences in intersection type (DDI versus standard intersection) and driving performance measures from the driving simulator. The only significant result was compliance to the speed limit (F (1, 656) = 160.11, p < 0.001) on the driving simulator. A higher proportion of red-light violations were observed by the researcher as participants navigated through the DDI, compared to the standard intersection. Qualitative comments from participants also highlighted the need for better signage and road markings. Recommendations when DDIs are implemented include community education on speed limit compliance, avoidance of red-light violations and design improvements regarding signage and road markings.     

Efficiently measuring recognition performance with sparse data

We examine methods for measuring performance in signal-detection-like tasks when each participant provides only a few observations. Monte Carlo simulations demonstrate that standard statistical techniques applied to ad’ analysis can lead to large numbers of Type I errors (incorrectly rejecting a hypothesis of no difference). Various statistical methods were compared in terms of their Type I and Type II error (incorrectly accepting a hypothesis of no difference) rates. Our conclusions are the same whether these two types of errors are weighted equally or Type I errors are weighted more heavily. The most promising method is to combine an aggregated’ measure with a percentile bootstrap confidence interval, a computerintensive nonparametric method of statistical inference. Researchers who prefer statistical techniques more commonly used in psychology, such as a repeated measurest test, should useγ (Goodman & Kruskal, 1954), since it performs slightly better than or nearly as well asd’. In general, when repeated measurest tests are used,γ is more conservative thand’: It makes more Type II errors, but its Type I error rate tends to be much closer to that of the traditional .05 α level. It is somewhat surprising thatγ performs as well as it does, given that the simulations that generated the hypothetical data conformed completely to thed’ model. Analyses in which H—FA was used had the highest Type I error rates. Detailed simulation results can be downloaded fromwww.psychonomic.org/archive/Schooler-BRM-2004.zip.     

Additivity and expected utility in risky multiattribute preferences

This experiment analyzed human preferences among even chance gambles for commodity bundles. The purpose of the experiment was to test several independence assumptions that distinguish between models for risky multiattribute preferences. In particular, the additivity and the expected utility part of the additive expected utility model were tested symmetrically. The degree and form of model violations were established, and the effects of instructions and of response modes were examined. All independence assumptions were violated by a bias to prefer a gamble or a commodity bundle that was previously matched against a standard. Systematic and strong violations that superseded this bias questioned the validity of the additive expected utility model. In violation of the additivity part of that model subjects consistently preferred the gamble with more balanced outcomes when comparing gambles with identical marginal probability distributions. This trend, called multiattribute risk aversion, was independent of subjects' single attribute risk attitude. Instructions and response modes had no noticeable impacts on these response patterns.     

Sublexical and syntactic processing during reading: evidence from eye movements of typically developing and dyslexic readers*

Skilled, typically developing readers and children with dyslexia read correct sentences and sentences that contained verb errors that were pseudo-homophones, morphological over-regularisations or syntactic errors. All errors increased looking time but the nature of the error and participant group influenced the time course of the effects. The pseudo-homophone effect was significant in all eye-movement measures for adults (N?=?26), intermediate (N?=?37) and novice typically developing readers (N?=?38). This effect was larger for intermediate readers than other groups in total duration. In contrast, morphological over-regularisations increased gaze and total duration (but not first fixation) for intermediate and novice readers, and only total duration for adult readers. Syntactic errors only increased total duration. Children with dyslexia (N?=?19) demonstrated smaller effects of pseudo-homophones and over-regularisations than controls, but their processing of syntactic errors was similar. We conclude that dyslexic children's difficulties with reading are linked to overreliance on phonological decoding and underspecified morphological processing, which impacts on word level reading. We highlight that the findings fit well within the grain-size model of word reading [Grainger, J., &; Ziegler, J. C. (2011). A dual-route approach to orthographic processing. Frontiers in Psychology, 2, 54. doi:10.3389/fpsyg.2011.00054].     

Revisiting Case IV: A reassessment of bias and standard errors of Case IV under range restriction

In 2004, Hunter and Schmidt proposed a correction (called Case IV) that seeks to estimate disattenuated correlations when selection is made on an unmeasured variable. Although Case IV is an important theoretical development in the range restriction literature, it makes an untestable assumption, namely that the partial correlation between the unobserved selection variable and the performance measure is zero. We show in this paper why this assumption may be difficult to meet and why previous simulations have failed to detect the full extent of bias. We use meta‐analytic literature to investigate the plausible range of bias. We also show how Case IV performs in terms of standard errors. Finally, we give practical recommendations about how the contributions of Hunter and Schmidt (2004) can be extended without making such stringent assumptions.     

Maybe not so independent after all: The possibility,prevalence, and consequences of violating the independence assumptions in psychometric meta-analysis

Psychometric meta-analysis assumes that moderators are unrelated to study artifacts (e.g., criterion reliability), and that study artifacts are independent of true validities. Meeting these assumptions is important for researchers seeking to accurately partition the variance in effect sizes due to study artifacts from the variance due to meaningful moderators. Despite the critical role of these assumptions, we know very little about their tenability. To address this basic gap in the literature, we conducted three studies to determine if there are potential violations of the independence assumptions (Study 1), the prevalence of such violations (Study 2), and the consequences of violating the independence assumptions via a series of Monte Carlo simulations (Study 3). We found that violations of the independence assumptions are not only plausible but also routinely detected across a wide array of topics. Simulation results indicate that violating the independence assumptions can result in biases under certain circumstances, which are further accentuated due to the lack of stability in the estimators. We offer suggestions for the future use of psychometric meta-analysis and discuss the implications for research focused on refining psychometric meta-analysis.     

Cognitive Bias for Pictorial and Linguistic Threat Cues in Children

The present study was designed to test the conditions under which threat-related cognitive bias can be observed in anxious children. Measures of cognitive bias for threatening words and pictures were obtained from spider fearful children (N = 55) and non-fearful children (N = 58) aged 8–11 in the first experiment, and from spider fearful children (N = 44), and two control groups (N = 41; N = 36) aged 8 in the second experiment. Cognitive bias was assessed by the emotional Stroop task. In line with our previous findings, all children aged 8 showed a bias for spider words, but not for spider pictures. However, a relation between spider fear and bias was observed when age was taken into account: bias for spider words decreased with age in the non-fearful children whereas this bias maintained in the fearful group. This differential age effect too replicated earlier findings (Kindt, Bierman, & Brosschot, 1997). It is suggested that a bias for threat words is a normal characteristic in children aged 8. During development, normal children learn to inhibit this processing bias, whilst fearful children fail to learn this ability.     

A Cathode Ray Oscillograph Apparatus for the Psychological Laboratory

A computer simulation which generated various sample distributions was performed. The equal interval assumption and degree of skewness were systematically manipulated to examine the sensitivity of the t and Mann-Whitney tests to varying degrees of violations of these t test assumptions. Results show that of the 348 sets (2436 t tests), 94 (27%) contained shifts from significant to nonsignificant t test values under mild violations of the equal-interval and normality assumption and would have led to decision errors. Of these, 55% would have been Type I and 45% would have been Type II. The implications of these results are discussed relative to Types I and II errors, to the use of parametric and nonparametric statistics, and to the likelihood of encountering such problems in sampled distributions.     

The time-saving bias, speed choices and driving behavior

When drivers are asked to estimate how much time can be saved by increasing speed, they generally underestimate the time saved when increasing from a relatively low speed and overestimate the time saved when increasing from a relatively high speed. This time-saving bias has been demonstrated to affect drivers’ estimations of driving speed as well as drivers’ personal choice of speed. Specifically, drivers with a high time-saving bias chose unduly high speeds, which sometimes results in speeding, more frequently than drivers with a lower degree of the bias. This study sought to determine whether this relationship would be mediated by individual differences in driving behavior – including drivers’ attitudes, norms and habits regarding speeding behavior as well as their level of aberrant driving behavior (committing aggressive or ordinary violations, errors or lapses in driving, measured by the Driver Behaviour Questionnaire). The results showed that the time-saving bias predicted estimations of required speed better than any of these factors, and also better than drivers’ age, gender, education and income, as well as the number of years they have had a license and their monthly driving kilometrage, their prior speeding violations and crash involvement. In predicting drivers’ personal speed choices, the time-saving bias was second only to the frequency of committing ordinary violations.     

Neural and behavioral effects of regulating emotional responses to errors during an implicit racial bias task

Affect regulation plays a key role in several theories of racial bias reduction. Here, we tested whether engaging in emotion regulation strategies while performing an implicit racial bias task (Weapons Identification Task; WIT) would alter neural and behavioral manifestations of bias. Participants either suppressed or reappraised in a positive light the distress associated with making errors during the WIT, while an electroencephalogram (EEG) was recorded. We hypothesized that engaging in emotion regulation strategies would reduce the distress associated with making errors indicative of bias, resulting in smaller error-related negativity (ERN) amplitude during errors and increased expression of racial bias. Results of within-subjects comparisons (Experiment 1) generally supported these predictions. However, when emotion regulation strategies were manipulated between subjects (Experiment 2) there was no effect of suppression or reappraisal on bias expression. Across both experiments, engaging in emotion regulation led to larger ERNs for errors occurring on Black- relative to White-primed trials. In addition, a number of significant order effects were observed, indicating important differences in the effects of engaging in emotion regulation strategies when those strategies are attempted in participants’ first versus second block of trials. No such order effects were evident when a second trial block was completed with no emotion regulation instructions. Findings are discussed in terms of the need for greater specificity in experimental tests of emotion regulation on error processing and cognitive performance.     

The Role of Scheduling in Learning Through Observation

In the 2 experiments reported in the present article, participants (N = 40, Experiment 1; N = 60, Experiment 2) learned to solve complex puzzles under different schedules of physical practice, observation, or a combination of the two. The results of both studies indicated that observation, in the absence of any physical practice, allows the development of an accurate but relatively nonfunctional cognitive representation. The data suggest that, even when the motor demands are minimal, the functional significance of the cognitive representation is not maximally realized until physical interaction with the task is possible. Thus, providing the participant with an interspersed practice schedule during acquisition enables that interaction to occur, thereby allowing the absolute number of physical practice trials to be reduced and replaced by observation trials, but leading to equivalent learning.     

Cross-cultural comparison of drivers’ tendency to commit different aberrant driving behaviours

The first aim of the present study was to identify key items which are rated differently by drivers from Finland, Sweden, Greece and Turkey. The second aim was to examine how these key items relate to drivers’ self-reported accident involvement. Similar comparisons have previously been conducted in Europe but these have only included items classified as violations and errors, but not lapses. A sample of Finnish (N = 200), Swedish (N = 200), Greek (N = 200) and Turkish (N = 200) drivers completed the driver behaviour questionnaire (DBQ) and reported their accident involvement during the previous 3 years. The results showed that nine key items (which drivers from different countries rated differently) could be identified. These items included two aggressive violations, four ordinary violations, three lapses, but no errors. Out of these nine items, five items (Become angered by a certain type of driver and indicate your hostility by whatever means you can, Disregard the speed limit on a motorway, Overtake a slow driver on the inside, Pull out of a junction so far that the driver with right of way has to stop and let you out and Get into the wrong lane approaching a roundabout or a junction) could explain differences in drivers’ self-reported yearly accident involvement when all four countries were taken together. At the same time, none of the items could explain differences in self-reported yearly accident involvement in Finland and Sweden while one of the items (Overtake a slow driver on the inside) could explain differences in self-reported yearly accident involvement in Greece and two of the items (Become angered by a certain type of driver and indicate your hostility by whatever means you can and Disregard the speed limit on a residential road) could explain differences in self-reported yearly accident involvement in Turkey. This shows that different countries have different problems with regard to aberrant driving behaviours which need to be taken into account when promoting traffic safety interventions and the driver behaviour questionnaire (DBQ) can be used to diagnose risk areas and to better inform road safety practitioners within and between countries.     

The specific role of spatial orientation skills in predicting driving behaviour

How people acquire environmental information brings out individual differences that are extremely large and robust. We assume that different spatial strategies used to represent, explore and move through the environment may predict risky driving behaviour.Here, we investigated spatial strategies and driving behaviour in 167 college students (86 women) using the following tests: the Spatial Cognitive Style Test, aimed at assessing spatial strategies characterized by different degrees of spatial competences (ranging from landmark, route to survey); the Manchester Driver Behavior Questionnaire, aimed at assessing errors, lapses, ordinary and aggressive Highway Code violations; the Attitude toward Road Safety Issues, aimed at assessing road safety attitudes related to driving.A series of regression analysis showed that spatial strategy used by drivers predicted the number of errors, lapses, ordinary and aggressive violations, as well as the number of road-safety behaviours. In conclusion, our results suggest that drivers preferring a survey strategy are much more able to make correct spatial decisions. Specifically, they are more confident about their spatial competence that in turn makes them less aggressive towards other drivers. Our findings suggest that good navigators travel without incurring in violations and fines. Implications regarding the possibility to use spatial navigational training to improve driving skills and release driving licence, as well as limitations of the study are discussed.     

Trends in Ethical Complaints Leading to Professional Counseling Licensing Boards Disciplinary Actions

This article presents findings of a content analysis of the types of ethical violations that led to disciplinary actions (N = 936) across state counseling licensing boards for professional counselors during the time frame of 2010 to 2014. The most frequent types of ethical violations included failure to acquire the appropriate amount of continuing education, dual relationships (sexual and nonsexual), and misrepresentation of credentials. Implications of the findings and future research needs are discussed.     

What Causes the Mean Bias of the Likelihood Ratio Statistic with Many Variables?

Abstract

Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. However, conventional SEM methods are not crafted to handle data with a large number of variables (p). A large p can cause T_ml, the most widely used likelihood ratio statistic, to depart drastically from the assumed chi-square distribution even with normally distributed data and a relatively large sample size N. A key element affecting this behavior of T_ml is its mean bias. The focus of this article is to determine the cause of the bias. To this end, empirical means of T_ml via Monte Carlo simulation are used to obtain the empirical bias. The most effective predictors of the mean bias are subsequently identified and their predictive utility examined. The results are further used to predict type I errors of T_ml. The article also illustrates how to use the obtained results to determine the required sample size for T_ml to behave reasonably well. A real data example is presented to show the effect of the mean bias on model inference as well as how to correct the bias in practice.     

Properties of bootstrap tests for N‐of‐1 studies

N‐of‐1 study designs involve the collection and analysis of repeated measures data from an individual not using an intervention and using an intervention. This study explores the use of semi‐parametric and parametric bootstrap tests in the analysis of N‐of‐1 studies under a single time series framework in the presence of autocorrelation. When the Type I error rates of bootstrap tests are compared to Wald tests, our results show that the bootstrap tests have more desirable properties. We compare the results for normally distributed errors with those for contaminated normally distributed errors and find that, except when there is relatively large autocorrelation, there is little difference between the power of the parametric and semi‐parametric bootstrap tests. We also experiment with two intervention designs: ABAB and AB, and show the ABAB design has more power. The results provide guidelines for designing N‐of‐1 studies, in the sense of how many observations and how many intervention changes are needed to achieve a certain level of power and which test should be performed.