Using bifactor models to identify faking on Big Five questionnaires

To identify faking, bifactor models were applied to Big Five personality data in three studies of laboratory and applicant samples using within‐subjects designs. The models were applied to homogenous data sets from separate honest, instructed faking, applicant conditions, and to simulated applicant data sets containing random individual responses from honest and faking conditions. Factor scores from the general factor in a bifactor model were found to be most highly related to response condition in both types of data sets. Domain factor scores from the faking conditions were found less affected by faking in measurement of Big Five domains than summated scale scores across studies. We conclude that bifactor models are efficacious in assessing the Big Five domains while controlling for faking.     

Exploring the interactions between modal options,destination access,and travel mood

A good understanding of subjectively experienced well-being in daily travel is critical for the design and evaluation of transportation policies and programs. Numerous studies have examined the relationship between how we travel and how we feel during travel. However, people’s travel mood is not only determined by their trip characteristics but also their potential mobility—the capacity of being mobile. Using the data collected via a smartphone application in Minneapolis-St. Paul area, we measure potential mobility in two aspects—modal options and destination access and reveal that people in households with better access to cars and alternative transportation modes are, all else equal, more likely to report more positive moods and fewer negative moods during travel. In addition, people living in neighborhoods with greater access to destinations are less likely to feel stress when traveling. We also find that associations between mode and mood are moderated by modal options and destination access. These findings confirm the importance of explicitly considering modal options and destination access in policy debates and planning initiatives addressing transportation and well-being. In doing so, they demonstrate the relevance of Sen’s Capabilities Approach to transportation planning.     

Adaptive sampling for active learning with genetic programming

Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically, often each generation, without considering the state of the evolution. In so doing, individuals do not have enough time to extract the hidden knowledge. An alternative approach is to use some information about the learning state to adapt the periodicity of the training data change. In this work, we propose an adaptive sampling strategy for classification tasks based on the state of solved fitness cases throughout learning. It is a flexible approach that could be applied with any dynamic sampling. We implemented some sampling algorithms extended with dynamic and adaptive controlling re-sampling frequency. We experimented them to solve the KDD intrusion detection and the Adult incomes prediction problems with GP. The experimental study demonstrates how the sampling frequency control preserves the power of dynamic sampling with possible improvements in learning time and quality. We also demonstrate that adaptive sampling can be an alternative to multi-level sampling. This work opens many new relevant extension paths.     

Estimation of a Latent Variable Regression Growth Curve Model for Individuals Cross-Classified by Clusters

The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units. This Monte Carlo study was conducted to evaluate model estimation under a variety of conditions and to measure the impact of ignoring cross-classified data when estimating the incorrectly specified HM3-LVR model in a scenario in which true values for parameters are known. Furthermore, results from a real-data analysis were used to inform the design of the simulation. Overall, it would be recommended for researchers to utilize the CCMM-LVR model over the HM3-LVR model when individuals are cross-classified, and to use a bare minimum of more than 100 clustering units in order to avoid overestimation of the level-3 variance component estimates.     

On the Computation of the RMSEA and CFI from the Mean-And-Variance Corrected Test Statistic with Nonnormal Data in SEM

A new type of nonnormality correction to the RMSEA has recently been developed, which has several advantages over existing corrections. In particular, the new correction adjusts the sample estimate of the RMSEA for the inflation due to nonnormality, while leaving its population value unchanged, so that established cutoff criteria can still be used to judge the degree of approximate fit. A confidence interval (CI) for the new robust RMSEA based on the mean-corrected (“Satorra-Bentler”) test statistic has also been proposed. Follow up work has provided the same type of nonnormality correction for the CFI (Brosseau-Liard & Savalei, 2014). These developments have recently been implemented in lavaan. This note has three goals: a) to show how to compute the new robust RMSEA and CFI from the mean-and-variance corrected test statistic; b) to offer a new CI for the robust RMSEA based on the mean-and-variance corrected test statistic; and c) to caution that the logic of the new nonnormality corrections to RMSEA and CFI is most appropriate for the maximum likelihood (ML) estimator, and cannot easily be generalized to the most commonly used categorical data estimators.     

Robustness of Parameter Estimation to Assumptions of Normality in the Multidimensional Graded Response Model

A central assumption that is implicit in estimating item parameters in item response theory (IRT) models is the normality of the latent trait distribution, whereas a similar assumption made in categorical confirmatory factor analysis (CCFA) models is the multivariate normality of the latent response variables. Violation of the normality assumption can lead to biased parameter estimates. Although previous studies have focused primarily on unidimensional IRT models, this study extended the literature by considering a multidimensional IRT model for polytomous responses, namely the multidimensional graded response model. Moreover, this study is one of few studies that specifically compared the performance of full-information maximum likelihood (FIML) estimation versus robust weighted least squares (WLS) estimation when the normality assumption is violated. The research also manipulated the number of nonnormal latent trait dimensions. Results showed that FIML consistently outperformed WLS when there were one or multiple skewed latent trait distributions. More interestingly, the bias of the discrimination parameters was non-ignorable only when the corresponding factor was skewed. Having other skewed factors did not further exacerbate the bias, whereas biases of boundary parameters increased as more nonnormal factors were added. The item parameter standard errors recovered well with both estimation algorithms regardless of the number of nonnormal dimensions.     

Predictive validity and efficiency of ongoing visual‐inspection criteria for interpreting functional analyses

Prior research has evaluated the reliability and validity of structured criteria for visually inspecting functional‐analysis (FA) results on a post‐hoc basis, after completion of the FA (i.e., post‐hoc visual inspection [PHVI]; e.g., Hagopian et al., 1997). However, most behavior analysts inspect FAs using ongoing visual inspection (OVI) as the FA is implemented, and the validity of applying structured criteria during OVI remains unknown. In this investigation, we evaluated the predictive validity and efficiency of applying structured criteria on an ongoing basis by comparing the interim interpretations produced through OVI with (a) the final interpretations produced by PHVI, (b) the authors’ post‐hoc interpretations (PHAI) reported in the research studies, and (c) the consensus interpretations of these two post‐hoc analyses. Ongoing visual inspection predicted the results of PHVI and the consensus interpretations with a very high degree of accuracy, and PHAI with a reasonably high degree of accuracy. Furthermore, the PHVI and PHAI results involved 32 FA sessions, on average, whereas the OVI required only 19 FA sessions to accurately identify the function(s) of destructive behavior (i.e., a 41% increase in efficiency). We discuss these findings relative to other methods designed to increase the accuracy and efficiency of FAs.     

Synergistic Information Processing Encrypts Strategic Reasoning in Poker

There is a tendency in decision‐making research to treat uncertainty only as a problem to be overcome. But it is also a feature that can be leveraged, particularly in social interaction. Comparing the behavior of profitable and unprofitable poker players, we reveal a strategic use of information processing that keeps decision makers unpredictable. To win at poker, a player must exploit public signals from others. But using public inputs makes it easier for an observer to reconstruct that player's strategy and predict his or her behavior. How should players trade off between exploiting profitable opportunities and remaining unexploitable themselves? Using a recent multivariate approach to information theoretic data analysis and 1.75 million hands of online two‐player No‐Limit Texas Hold'em, we find that the important difference between winning and losing players is not in the amount of information they process, but how they process it. In particular, winning players are better at integrative information processing—creating new information from the interaction between their cards and their opponents’ signals. We argue that integrative information processing does not just produce better decisions, it makes decision‐making harder for others to reverse engineer, as an expert poker player's cards act like the private key in public‐key cryptography. Poker players encrypt their reasoning with the way they process information. The encryption function of integrative information processing makes it possible for players to exploit others while remaining unexploitable. By recognizing the act of information processing as a strategic behavior in its own right, we offer a detailed account of how experts use endemic uncertainty to conceal their intentions in high‐stakes competitive environments, and we highlight new opportunities between cognitive science, information theory, and game theory.     

CONTINUOUS RECORDING AND INTEROBSERVER AGREEMENT ALGORITHMS REPORTED IN THE JOURNAL OF APPLIED BEHAVIOR ANALYSIS (1995–2005)

We reviewed all research articles in 10 recent volumes of the Journal of Applied Behavior Analysis (JABA): Vol. 28(3), 1995, through Vol. 38(2), 2005. Continuous recording was used in the majority (55%) of the 168 articles reporting data on free‐operant human behaviors. Three methods for reporting interobserver agreement (exact agreement, block‐by‐block agreement, and time‐window analysis) were employed in more than 10 of the articles that reported continuous recording. Having identified these currently popular agreement computation algorithms, we explain them to assist researchers, software writers, and other consumers of JABA articles.     

COMPARISON OF DATA‐COLLECTION METHODS IN A BEHAVIORAL INTERVENTION PROGRAM FOR CHILDREN WITH PERVASIVE DEVELOPMENTAL DISORDERS: A REPLICATION

Cummings and Carr (2009) compared two methods of data collection in a behavioral intervention program for children with pervasive developmental disorders: collecting data on all trials versus only the first trial in a session. Results showed that basing a child's progress on first‐trial data resulted in identifying mastery‐level responding slightly sooner, whereas determining mastery based on all trials resulted in slightly better skill maintenance. In the current replication, no such differences in indication of mastery or maintenance were observed when data were collected on all trials or the first trial.