Generating correlation matrices with model error for simulation studies in factor analysis: A combination of the Tucker-Koopman-Linn model and Wijsman’s algorithm

Most simulation studies in factor analysis follow a process of constructing population correlation matrices from the common-factor model and generating sample correlation matrices from the population matrices. In the common-factor model, the population correlation matrix is perfectly fit by the model’s containing common and unique factors. However, since no mathematical model accounts exactly for the real-world phenomena that it is intended to represent, the Tucker-Koopman-Linn model (1969) is more realistic for generating correlation matrices than the conventional common-factor model because the former incorporates model error. In this paper, a procedure for generating population and sample correlation matrices with model error by combining the Tucker-Koopman-Linn model and Wijsman’s algorithm (1959) is presented. The SAS/ IML program for generating correlation matrices is described, and an example is also provided.     

The adjusted rand statistic: A SAS macro

A macro for calculating the Hubert and Arabie (1985) adjusted Rand statistic is presented. The adjusted Rand statistic gives a measure of classification agreement between two partitions of the same set of objects. The macro is written in the SAS macro language and makes extensive use of SAS/IML software (SAS Institute, 1985a, 1985b). The macro uses two different methods of handling missing values. The default method assumes that each object that has a missing value for the classification category is in its own separate category or cluster for that classification. The optional method places all objects with a missing value for the classification category into the same category for that classification.This study was supported in part by Individual National Research Service Award F32 DA 05283 from the National Institute on Drug Abuse.Requests for the Macro code can be sent via BITNET: CUSGPXH @ UCLAMVS. A copy of the macrocode can also be obtained by sending a stamped self-addressed mailer and a PC-DOS formatted floppy diskette to Paul Hoffman, 5628 MSA, UCLA, Los Angeles, CA 90024-1557.     

A statistical analysis system (SAS) computer program for Markov chain analysis

A computer program for Markov chain analysis is presented and discussed. The program is written in the language of the Statistical Analysis System (SAS) but detailed knowledge of SAS is not required for its use. The program tests hypotheses about the goodness of fit of first- and second-order Markov models. It also tests if transition probabilities are homogeneous between the first and the second half of each sequence.Presented, in part, at the Association for the Advancement of Behavior Therapy Convention, November 1984.     

Internal Reformation Within the Contemporary Coptic Imagined Community: The Sunday School Movement and Mechanisms of Minority Survival

Abstract

With the centennial jubilee of the Sunday School Movement (SSM) occurring in 2018, this article explores two main questions: what are the main features of the Coptic community as imagined by the leaders of the SSM? And what is the nature of the relationship between “Coptic” Christian and “national Egyptian” identities within the dominant discourse of the SSM? The article argues that the contemporary Coptic identity, as reconstituted by the SSM, helps the Coptic community to survive as a vertical ethnie. The new Coptic identity is rooted in and relies upon the exclusive use of demotic symbols and narratives. Thus equipped, modern Copts perceive themselves as part of an imagined spiritual community within the wider Egyptian community. Indeed, this article argues that the SSM’s discourse presents a unique ‘marble cake’ model wherein religious and national identities are both present. By portraying Coptism as the area of interplay between Christianity and Egyptian-ness, the SSM blends “biological” and “cultural-ideological” modes of myth-making. Accordingly, to identify as Copt becomes equivalent to identifying as Egyptian.     

SEM with Missing Data and Unknown Population Distributions Using Two-Stage ML: Theory and Its Application

This article provides the theory and application of the 2-stage maximum likelihood (ML) procedure for structural equation modeling (SEM) with missing data. The validity of this procedure does not require the assumption of a normally distributed population. When the population is normally distributed and all missing data are missing at random (MAR), the direct ML procedure is nearly optimal for SEM with missing data. When missing data mechanisms are unknown, including auxiliary variables in the analysis will make the missing data mechanism more likely to be MAR. It is much easier to include auxiliary variables in the 2-stage ML than in the direct ML. Based on most recent developments for missing data with an unknown population distribution, the article first provides the least technical material on why the normal distribution-based ML generates consistent parameter estimates when the missing data mechanism is MAR. The article also provides sufficient conditions for the 2-stage ML to be a valid statistical procedure in the general case. For the application of the 2-stage ML, an SAS IML program is given to perform the first-stage analysis and EQS codes are provided to perform the second-stage analysis. An example with open- and closed-book examination data is used to illustrate the application of the provided programs. One aim is for quantitative graduate students/applied psychometricians to understand the technical details for missing data analysis. Another aim is for applied researchers to use the method properly.     

State-Space Analysis of Working Memory in Schizophrenia: An FBIRN Study

The neural correlates of working memory (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task conditions, important hypotheses regarding the representation of mental processes in the spatio-temporal patterns of neural recruitment and the differential organization of these mental processes in patients versus controls have not been addressed in this context. This paper uses a multivariate state-space model (SSM) to analyze the differential representation and organization of mental processes of controls and patients performing the Sternberg Item Recognition Paradigm (SIRP) WM task. The SSM is able to not only predict the mental state of the subject from the data, but also yield estimates of the spatial distribution and temporal ordering of neural activity, along with estimates of the hemodynamic response. The dynamical Bayesian modeling approach used in this study was able to find significant differences between the predictability and organization of the working memory processes of SZ patients versus healthy subjects. Prediction of some stimulus types from imaging data in the SZ group was significantly lower than controls, reflecting a greater level of disorganization/heterogeneity of their mental processes. Moreover, the changes in accuracy of predicting the mental state of the subject with respect to parametric modulations, such as memory load and task duration, may have important implications on the neurocognitive models for WM processes in both SZ and healthy adults. Additionally, the SSM was used to compare the spatio-temporal patterns of mental activity across subjects, in a holistic fashion and to derive a low-dimensional representation space for the SIRP task, in which subjects were found to cluster according to their diagnosis.     

Secret Agent Society: A Randomized Controlled Trial of a Transdiagnostic Youth Social Skills Group Treatment

Group programs are key for targeting social skills (SS) for children with developmental disorders and/or mental illness. Despite promising evidence regarding efficacy of group treatments, there are several limitations to current research regarding generalizability and effectiveness across diagnoses. This randomized control trial assessed whether the Secret Agent Society (SAS) group program was superior to treatment as usual (TAU) in improving social-emotional functioning for children with Attention Deficit-Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and/or anxiety. Eighty-nine youth (8–12) with ADHD, ASD, and/or an anxiety disorder receiving treatment at hospital-based outpatient clinics were randomized to receive SAS (n?=?47) or TAU (n?=?42) over a three-month period, at which point TAU participants were offered the SAS intervention. Parent report showed significant improvement in Emotion Regulation (ER) and Social Skills (SS) for youth in SAS vs. TAU (Fs?≥?6.79, ps?≤?01). Gains for the SAS condition were maintained at 6-months. Intent-to-treat analysis of teacher report indicated youth in SAS had positive gains in SS (F?=?0.41, p?=?0.475) and ER (F?=?0.99, p?=?0.322), though not significantly better than youth in TAU. Clinically reliable improvement rates were significantly higher for SAS participants than TAU for parent and teacher reported SS and ER. Improvements were significant for youth with single and comorbid diagnoses. Results suggest that SAS was superior to TAU in improving SS and ER for youth aged 8–12 with ADHD, ASD, and/or anxiety. Gains maintained in the medium-term. Trial registration number NCT02574273, registered 10/12/2015.

     

Satisfaction at last: Evidence for the “satisfaction” hypothesis for multiple-target search errors

ABSTRACT

Multiple-target visual searches are susceptible to Subsequent Search Miss (SSM) errors—a reduced accuracy for target detection after a previous target has already been detected. SSM errors occur in critical searches (e.g., evaluations of radiographs and airport luggage x-rays), and have proven to be a stubborn problem. A few SSM theories have been offered, and here we investigate the “satisfaction” account: failing to completely finish a search after having found a first target. Accuracy on a multiple-target search task was compared to both how long participants spent searching after finding a first target and their target sensitivity in a separate vigilance task. Less time spent searching and poor vigilance predicted higher SSM error rates. These results suggest that observers who are more likely to miss a second target are less likely to thoroughly search after finding a first target, thus offering some of the first evidence for the “satisfaction” account.     

Estimation of the MIRID: A program and a SAS-based approach

The MIRID CML program is a program for the estimation of the parameter values of two different componential IRT models: the Rasch—MIRID and the OPLM—MIRID (Butter, 1994; Butter, De Boeck, & Verhelst, 1998). To estimate the parameters of both models, the program uses a CML approach. The model parameters can also be estimated with a MML approach that can be implemented in PROC NLMIXED of SAS Version 8. Both the MIRID CML program and the MML SAS approach are explained and compared in a simulation study. The results showed that they did about equally well in estimating the values of the item parameters but that there were some differences in the estimation of the person parameters, as could be expected from the differential assumptions regarding the distribution of the persons. The SAS MML approach is much slower than the MIRID CML program, but it is more flexible.     

Bio-inspired cognitive model of motor learning by imitation

Learning, and even more so by imitation, is an essential Cognitive Functions because it is carried out throughout life and allows us to adapt our behaviors from other beings through observation. In this work, we propose a model, and implementation of the cognitive function of imitation motor learning (IML), based on psychological and neuroscientific evidence. According to the evidence, learning by imitation includes imitation of action and imitation of action over an object sub-processes. The imitation of action consists of the movement of the limbs. The imitation of action over an object consists of the interaction with an object within the environment. We achieve an implementation of the proposed model for IML and endow a virtual entity with it. In order to validate the proposal, we use a case study to analyze the sub-processes performance. From results, we conclude that both imitation of action and imitation of action over an object sub-processes play an essential role in getting the agent to interact with stimuli within the environment.     

CTTITEM: SAS macro and SPSS syntax for classical item analysis

This article describes the functions of a SAS macro and an SPSS syntax that produce common statistics for conventional item analysis including Cronbach’s alpha, item difficulty index (p-value or item mean), and item discrimination indices (D-index, point biserial and biserial correlations for dichotomous items and item-total correlation for polytomous items). These programs represent an improvement over the existing SAS and SPSS item analysis routines in terms of completeness and user-friendliness. To promote routine evaluations of item qualities in instrument development of any scale, the programs are available at no charge for interested users. The program codes along with a brief user’s manual that contains instructions and examples are downloadable from suen.ed.psu.edu/~pwlei/plei.htm.     

An SAS/IML procedure for maximum likelihood factor analysis

Maximum likelihood factor analysis (MLFA), originally introduced by Lawley (1940), is based on a firm mathematical foundation that allows hypothesis testing when normality is assumed with large sample sizes. MLFA has gained in popularity since Jöreskog (1967) implemented an iterative algorithm to estimate parameters. This article presents a concise program using matrix language SAS/LML with the optimization subroutine NLPQN to obtain MLFA solutions. The program is pedagogically useful because it shows the step-by-step computational processes for MLFA, whereas almost all other statistical packages for MLFA are in “black boxes.” It is also demonstrated that this approach can be extended to other multivariate methods requiring numerical optimizations, such as the widely used structural equation modeling. Researchers may find this program useful in conducting Monte Carlo simulation studies to investigate the properties of multivariate methods that involve numerical optimizations.     

A Method of Correcting Estimation Failure in Latent Differential Equations with Comparisons to Kalman Filtering

Abstract

Studies have used the latent differential equation (LDE) model to estimate the parameters of damped oscillation in various phenomena, but it has been shown that correct, non-zero parameter estimates are only obtained when the latent series exhibits little or no process noise. Consequently, LDEs are limited to modeling deterministic processes with measurement error rather than those with random behavior in the true latent state. The reasons for these limitations are considered, and a piecewise deterministic approximation (PDA) algorithm is proposed to treat process noise outliers as functional discontinuities and obtain correct estimates of the damping parameter. Comprehensive, random-effects simulations were used to compare results with those obtained using a state-space model (SSM) based on the Kalman filter. The LDE with the PDA algorithm (LDEPDA) successfully recovered the simulated damping parameter under a variety of conditions when process noise was present in the latent state. The LDEPDA had greater precision and accuracy than the SSM when estimating parameters from data with sparse jump discontinuities, but worse performance for diffusion processes overall. All three methods were applied to a sample of postural sway data. The basic LDE estimated zero damping, while the LDEPDA and SSM estimated moderate to high damping. The SSM estimated the smallest standard errors for both frequency and damping parameter estimates.     

Distribution of the product confidence limits for the indirect effect: Program PRODCLIN

This article describes a program, PRODCLIN (distribution of the PRODuct Confidence Limits for INdirect effects), written for SAS, SPSS, and R, that computes confidence limits for the product of two normal random variables. The program is important because it can be used to obtain more accurate confidence limits for the indirect effect, as demonstrated in several recent articles (MacKinnon, Lockwood, & Williams, 2004; Pituch, Whittaker, & Stapleton, 2005). Tests of the significance of and confidence limits for indirect effects based on the distribution of the product method have more accurate Type I error rates and more power than other, more commonly used tests. Values for the two paths involved in the indirect effect and their standard errors are entered in the PRODCLIN program, and distribution of the product confidence limits are computed. Several examples are used to illustrate the PRODCLIN program. The PRODCLIN programs in rich text format may be downloaded from www.psychonomic.org/archive.     

An Easy and Practical Approach to Data Management

This article demonstrates the usefulness and practicality of the Statistical Analysis System (SAS) (1979, version 79.5) as a data base management system for a developmental studies program. SAS is available on IBM computers at educational institutions across the county, and it provides an effective tool for data management.     

Personality factors in dysphoria: A psychometric refinement of beck's Sociotropy-Autonomy Scale

The Sociotropy-Autonomy Scale (SAS) was developed as a means of assessing two cognitive-personality constructs postulated as vulnerability factors in depression (Beck, 1983). While studies have confirmed the validity of the SAS Sociotropy Scale, the construct validity of the Autonomy Scale has not been well supported. The present study was conducted to improve on the measurement of autonomy by generating additional items which were added to the original questionnaire. This 93-item SAS was administered to 485 undergraduates along with the Beck Depression Inventory, Beck Anxiety Inventory, PRF Affiliation and Autonomy Scales, Positive and Negative Emotionality Measures, and Marlowe-Crowne Social Desirability Scale. Four factors labeled Sociotropy, Solitude/Interpersonal Insensitivity, Independence, and Individualistic Achievement emerged from a principal-factor analysis of the SAS item pool. Hierarchical multiple regression analyses indicated that positive emotionality and solitude/interpersonal insensitivity were specifically related to dysphoria, while negative emotionality and sociotropy had significant associations with both anxious and depressed mood states.This research was supported by University of New Brunswick Research Grant 22–51 awarded to the first author.     

The use of matrix manipulation programs in teaching statistical methods

An array manipulation package, the Symbolic Matrix Interpretive System and Statistical Interpretive System (SMIS/SIS), is described. Using SMIS/SIS, one can declare matrices and arrays of real or randomly generated data and then manipulate the matrices using simple commands to produce virtually any desired analysis. The package, designed for use by nonprogrammers and programmers alike, features a full complement of matrix algebra operations and a smaller set of statistical functions. The package, while useful as a research tool, is especially useful in teaching statistical methods, particularly multivariate analysis in which the correspondence between mathematical formulas and program code has definite pedagogical advantages over the use of statistical program packages like SPSS, BMDP, and SAS.     

Pitfalls in computing and interpreting randomization testp values: A commentary on Chen and Dunlap

Chen and Dunlap (1993) added to the growing list of papers promoting the use of randomization tests in statistical testing. Their particular contribution was an SAS program that could bring computation of these tests to a wider audience. The present paper points to several problems with the presentation of Chen and Dunlap and provides solutions to these problems. It is concluded that randomization tests deserve more attention, but that they are best computed by programs written in a low-level programming language or, if using SAS on a mainframe, by using the MULTTEST procedure.