An R package for state-trace analysis

State-trace analysis (Bamber, Journal of Mathematical Psychology, 19, 137-181, 1979) is a graphical analysis that can determine whether one or more than one latent variable mediates an apparent dissociation between the effects of two experimental manipulations. State-trace analysis makes only ordinal assumptions and so, is not confounded by range effects that plague alternative methods, especially when performance is measured on a bounded scale (such as accuracy). We describe and illustrate the application of a freely available GUI driven package, StateTrace, for the R language. StateTrace automates many aspects of a state-trace analysis of accuracy and other binary response data, including customizable graphics and the efficient management of computationally intensive Bayesian methods for quantifying evidence about the outcomes of a state-trace experiment, developed by Prince, Brown, and Heathcote (Psychological Methods, 17, 78-99, 2012).     

An R package for single-case randomization tests

Randomization tests are nonparametric statistical tests that obtain their validity by computationally mimicking the random assignment procedure that was used in the design phase of a study. Because randomization tests do not rely on a random sampling assumption, they can provide a better alternative than parametric statistical tests for analyzing data from single-case designs. In this article, an R package is described for use in designing single-case phase (AB, ABA, and ABAB) and alternation (completely randomized, alternating treatments, and randomized block) experiments, as well as for conducting statistical analyses on data gathered by means of such designs. The R code is presented in a step-by-step way, which at the same time clarifies the rationale behind single-case randomization tests.     

Mousetrap: An integrated,open-source mouse-tracking package

Mouse-tracking – the analysis of mouse movements in computerized experiments – is becoming increasingly popular in the cognitive sciences. Mouse movements are taken as an indicator of commitment to or conflict between choice options during the decision process. Using mouse-tracking, researchers have gained insight into the temporal development of cognitive processes across a growing number of psychological domains. In the current article, we present software that offers easy and convenient means of recording and analyzing mouse movements in computerized laboratory experiments. In particular, we introduce and demonstrate the mousetrap plugin that adds mouse-tracking to OpenSesame, a popular general-purpose graphical experiment builder. By integrating with this existing experimental software, mousetrap allows for the creation of mouse-tracking studies through a graphical interface, without requiring programming skills. Thus, researchers can benefit from the core features of a validated software package and the many extensions available for it (e.g., the integration with auxiliary hardware such as eye-tracking, or the support of interactive experiments). In addition, the recorded data can be imported directly into the statistical programming language R using the mousetrap package, which greatly facilitates analysis. Mousetrap is cross-platform, open-source and available free of charge from https://github.com/pascalkieslich/mousetrap-os.     

Desequens: An R package for the variance decomposition of sequential processes

Recently, Kuppens, Van Mechelen, and Rijmen (2008) developed a method that allows researchers to examine and disentangle the contributions of the different possible sources of variability in sequential processes that underlie psychological outcomes or behaviors. Although this method may prove valuable for many research domains in the social sciences, its use may be limited by its statistical complexity and the effort and programming skills required. We present an R package, called Desequens, intended to make this method easily accessible to social science researchers. The tool does not require any knowledge of R, so that R laymen can easily apply the method to their data as well. We demonstrate the use of Desequens by means of a didactic example.     

Quint: An R package for the identification of subgroups of clients who differ in which treatment alternative is best for them

In the analysis of randomized controlled trials (RCTs), treatment effect heterogeneity often occurs, implying differences across (subgroups of) clients in treatment efficacy. This phenomenon is typically referred to as treatment-subgroup interactions. The identification of subgroups of clients, defined in terms of pretreatment characteristics that are involved in a treatment-subgroup interaction, is a methodologically challenging task, especially when many characteristics are available that may interact with treatment and when no comprehensive a priori hypotheses on relevant subgroups are available. A special type of treatment-subgroup interaction occurs if the ranking of treatment alternatives in terms of efficacy differs across subgroups of clients (e.g., for one subgroup treatment A is better than B and for another subgroup treatment B is better than A). These are called qualitative treatment-subgroup interactions and are most important for optimal treatment assignment. The method QUINT (Qualitative INteraction Trees) was recently proposed to induce subgroups involved in such interactions from RCT data. The result of an analysis with QUINT is a binary tree from which treatment assignment criteria can be derived. The implementation of this method, the R package quint, is the topic of this paper. The analysis process is described step-by-step using data from the Breast Cancer Recovery Project, showing the reader all functions included in the package. The output is explained and given a substantive interpretation. Furthermore, an overview is given of the tuning parameters involved in the analysis, along with possible motivational concerns associated with choice alternatives that are available to the user.     

TripleR: An R package for social relations analyses based on round-robin designs

In this article, we present TripleR, an R package for the calculation of social relations analyses (Kenny, 1994) based on round-robin designs. The scope of existing software solutions is ported to R and enhanced with previously unimplemented methods of significance testing in single groups (Lashley & Bond, 1997) and handling of missing values. The package requires only minimal knowledge of R, and results can be exported for subsequent analyses to other software packages. We demonstrate the use of TripleR with several didactic examples.     

Fitting Wald and ex-Wald distributions to response time data: An example using functions for the S-PLUS package

Schwarz (2001, 2002) proposed the ex-Wald distribution, obtained from the convolution of Wald and exponential random variables, as a model of simple and go/no-go response time. This article provides functions for the S-PLUS package that produce maximum likelihood estimates of the parameters for the ex-Wald, as well as for the shifted Wald and ex-Gaussian, distributions. In a Monte Carlo study, the efficiency and bias of parameter estimates were examined. Results indicated that samples of at least 400 are necessary to obtain adequate estimates of the ex-Wald and that, for some parameter ranges, much larger samples may be required. For shifted Wald estimation, smaller samples of around 100 were adequate, at least when fits identified by the software as having ill-conditioned maximums were excluded. The use of all functions is illustrated using data from Schwarz (2001). The S-PLUS functions and Schwarz’s data may be downloaded from the Psychonomic Society’s Web archive, www. psychonomic.org/archive/.     

A general framework and an R package for the detection of dichotomous differential item functioning

Differential item functioning (DIF) is an important issue of interest in psychometrics and educational measurement. Several methods have been proposed in recent decades for identifying items that function differently between two or more groups of examinees. Starting from a framework for classifying DIF detection methods and from a comparative overview of the most traditional methods, an R package for nine methods, called difR, is presented. The commands and options are briefly described, and the package is illustrated through the analysis of a data set on verbal aggression.     

Olp: An R package for optimal linear partitions of finite sets of points on the plane

Given a set of points on the plane and an assignment of values to them, an optimal linear partition is a division of the set into two subsets which are separated by a straight line and maximally contrast with each other in the values assigned to their points. We present a method for inspecting and rating all linear partitions of a finite set, and a package of three functions in the R language for executing the computations. One function is for finding the optimal linear partitions and corresponding separating lines, another for graphically representing the results, and a third for testing how well the data comply with the linear separability condition. We illustrate the method on possible data from a psychophysical experiment (concerning the size–weight illusion) and compare its performance with that of linear discriminant analysis and multiple logistic regression, adapted to dividing linearly a set of points on the plane.     

An instructional package to teach communication behaviors in a classroom setting

An instructional package consisting of modeling, written and behavioral rehearsal, and feedback was used to teach communication behaviors (simple sentence responses, information responses, and eye contact) to 17 emotionally handicapped students, ages ranging between 11 and 17 years. A multiple baseline analysis indicated that the communication behaviors targeted for training increased significantly following implementation of the instructional package. These behaviors also generalized across settings and nontrained questions and were maintained across time. Finally, the average amount of training time required for each subject was 2 hours, indicating that the use of the instructional package in a classroom setting was economical. These results show that communication behaviors can be delivered effectively and economically in a classroom setting.     

An Apple II package for computer-assisted telephone interviewing

This paper describes an Apple II (an Apple-compatible) microcomputer package for a computer-assisted telephone interviewing (CATI) system. This system generates random telephone numbers within any number of exchanges that the user supplies, automatically dials the telephone, prompts the interviewer with each question, and records and permanently saves each response. It also provides a printed copy of the collected data by frequency of response in each question category. The Apple II CATI package is readily available, inexpensive, and easy to program in the BASIC language.     

The microanalytic data analysis package

The Microanalytic Data Analysis Package is a series of BASIC programs designed to facilitate the analysis of observational data. The package provides: (1) an organizational framework for microanalytic data that is common to all programs; (2) reliability, frequency, cooccurrence, latency, distribution, and lag sequential analysis programs; and (3) data reduction programs.     

Randomization tests for multiple-baseline designs: An extension of the SCRT-R package

Multiple-baseline designs are an extension of the basic single-case AB phase designs, in which several of those AB designs are implemented simultaneously to different persons, behaviors, or settings, and the intervention is introduced in a staggered way to the different units. These designs are well-suited for research in the behavioral sciences. We discuss the advantages and limitations for valid inferences, and suggest a statistical technique—randomization tests—for use with multiple-baseline data, to complement visual analysis. In addition, we provide an extension of our SCRT-R package (which already contained means for conducting randomization tests on single-case phase and alternation designs), for multiple-baseline AB data.     

RMediation: An R package for mediation analysis confidence intervals

This article describes the RMediation package,which offers various methods for building confidence intervals (CIs) for mediated effects. The mediated effect is the product of two regression coefficients. The distribution-of-the-product method has the best statistical performance of existing methods for building CIs for the mediated effect. RMediation produces CIs using methods based on the distribution of product, Monte Carlo simulations, and an asymptotic normal distribution. Furthermore, RMediation generates percentiles, quantiles, and the plot of the distribution and CI for the mediated effect. An existing program, called PRODCLIN, published in Behavior Research Methods, has been widely cited and used by researchers to build accurate CIs. PRODCLIN has several limitations: The program is somewhat cumbersome to access and yields no result for several cases. RMediation described herein is based on the widely available R software, includes several capabilities not available in PRODCLIN, and provides accurate results that PRODCLIN could not.     

BootES: An R package for bootstrap confidence intervals on effect sizes

Bootstrap Effect Sizes (bootES; Gerlanc & Kirby, 2012) is a free, open-source software package for R (R Development Core Team, 2012), which is a language and environment for statistical computing. BootES computes both unstandardized and standardized effect sizes (such as Cohen’s d, Hedges’s g, and Pearson’s r) and makes easily available for the first time the computation of their bootstrap confidence intervals (CIs). In this article, we illustrate how to use bootES to find effect sizes for contrasts in between-subjects, within-subjects, and mixed factorial designs and to find bootstrap CIs for correlations and differences between correlations. An appendix gives a brief introduction to R that will allow readers to use bootES without having prior knowledge of R.     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.     

Methods for the Behavioral,Educational, and Social Sciences: An R package

Methods for the Behavioral, Educational, and Social Sciences (MBESS; Kelley, 2007b) is an open source package for R (R Development Core Team, 2007b), an open source statistical programming language and environment. MBESS implements methods that are not widely available elsewhere, yet are especially helpful for the idiosyncratic techniques used within the behavioral, educational, and social sciences. The major categories of functions are those that relate to confidence interval formation for noncentral t, F, and chi2 parameters, confidence intervals for standardized effect sizes (which require noncentral distributions), and sample size planning issues from the power analytic and accuracy in parameter estimation perspectives. In addition, MBESS contains collections of other functions that should be helpful to substantive researchers and methodologists. MBESS is a long-term project that will continue to be updated and expanded so that important methods can continue to be made available to researchers in the behavioral, educational, and social sciences.     

Fitting wald and ex-Wald distributions to response time data: an example using functions for the S-PLUS package.

Schwarz (2001, 2002) proposed the ex-Wald distribution, obtained from the convolution of Wald and exponential random variables, as a model of simple and go/no-go response time. This article provides functions for the S-PLUS package that produce maximum likelihood estimates of the parameters for the ex-Wald, as well as for the shifted Wald and ex-Gaussian, distributions. In a Monte Carlo study, the efficiency and bias of parameter estimates were examined. Results indicated that samples of at least 400 are necessary to obtain adequate estimates of the ex-Wald and that, for some parameter ranges, much larger samples may be required. For shifted Wald estimation, smaller samples of around 100 were adequate, at least when fits identified by the software as having ill-conditioned maximums were excluded. The use of all functions is illustrated using data from Schwarz (2001). The S-PLUS functions and Schwarz's data may be downloaded from the Psychonomic Society's Web archive, www. psychonomic.org/archive/.     

Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

The paper develops a two-stage robust procedure for structural equation modeling (SEM) and an R package rsem to facilitate the use of the procedure by applied researchers. In the first stage, M-estimates of the saturated mean vector and covariance matrix of all variables are obtained. Those corresponding to the substantive variables are then fitted to the structural model in the second stage. A sandwich-type covariance matrix is used to obtain consistent standard errors (SE) of the structural parameter estimates. Rescaled, adjusted as well as corrected and F-statistics are proposed for overall model evaluation. Using R and EQS, the R package rsem combines the two stages and generates all the test statistics and consistent SEs. Following the robust analysis, multiple model fit indices and standardized solutions are provided in the corresponding output of EQS. An example with open/closed book examination data illustrates the proper use of the package. The method is further applied to the analysis of a data set from the National Longitudinal Survey of Youth 1997 cohort, and results show that the developed procedure not only gives a better endorsement of the substantive models but also yields estimates with uniformly smaller standard errors than the normal-distribution-based maximum likelihood.