Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.

https://epub.ub.uni-muenchen.de/10589/1/partitioning.pdf

An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests

Two simulation studies were conducted to investigate the effects of the practice of item parceling. In Study 1, unidimensional sets of normally and nonnormally distributed item-level data were categorized into 2-, 3-, and 4-item parcels. Analyses revealed that the use of item parcels resulted in better fitting solutions, as measured by the root mean squared error of approximation (RMSEA), comparative fit index (CFI), and chi-square test, when items had a unidimensional structure. Parceled solutions also resulted in less bias in estimates of structural parameters under these conditions than did solutions based on the individual items. In Study 2 the issue of whether the use of item parceling could mask a known multidimensional factor structure among a set of items was investigated. Results indicated that certain types of item parceling can obfuscate a multidimensional factor structure in such a way that acceptable values of fit indexes are found for a misspecified solution. In addition, parceling under the...

/pdf/the-effects-of-item-parceling-on-goodness-of-fit-and-3p38qzjc3u.pdf

The Effects of Item Parceling on Goodness-of-Fit and Parameter Estimate Bias in Structural Equation Modeling

For theoretical and empirical reasons, researchers may combine item-level responses into aggregate item parcels to use as indicators in a structural equation modeling context. Yet the effects of specific parceling strategies on parameter estimation and model fit are not known. In Study 1, different parceling combinations meaningfully affected parameter estimates and fit indicators in two organizational data sets. Based on the concept of external consistency, the authors proposed that combining items that shared an unmodeled secondary influence into the same parcel (shared uniqueness strategy) would enhance the accuracy of parameter estimates. This proposal was supported in Study 2, using simulated data generated from a known model. When the unmodeled secondary influence was related to indicators of only one latent construct, the shared uniqueness parceling strategy resulted in more accurate parameter estimates. When indicators of both target latent constructs were contaminated, bias was present but approp...

Item Parceling Strategies in SEM: Investigating the Subtle Effects of Unmodeled Secondary Constructs:

This article illustrates the use of structural equation modeling (SEM) procedures with latent variables to analyze data from experimental studies. These procedures allow the researcher to remove the biasing effects of random and correlated measurement error on the outcomes of the experiment and to examine processes that may account for changes in the outcome variables that are observed. Analyses of data from a Project Family study, an experimental intervention project with rural families that strives to improve parenting skills, are presented to illustrate the use of these modeling procedures. Issues that arise in applying SEM procedures, such as sample size and distributional characteristics of the measures, are discussed.

Analyzing data from experimental studies: A latent variable structural equation modeling approach.

Most college students are required to enroll in statistics and quantitative research methodology courses as a necessary part of their degree programmes. Unfortunately, many students report high levels of statistics anxiety while enrolled in these classes. Recent years have seen an increase in the number of articles on statistics anxiety appearing in the literature, as researchers have recognised that statistics anxiety is a multidimensionality construct that has debilitative effects on academic performance. Thus, the purpose of this article is to provide a comprehensive summary of the literature on statistics anxiety. In particular, the nature, etiology, and prevalence of statistics anxiety are described. Additionally, antecedents (i.e. dispositional, situational and environmental) of statistics anxiety are identified, as well as their effects on statistics achievement. Furthermore, existing measures of statistics anxiety are documented. Finally, based on the literature, successful interventions for reduc...

/pdf/statistics-anxiety-nature-etiology-antecedents-effects-and-43ac2kdywc.pdf

Statistics Anxiety: Nature, etiology, antecedents, effects, and treatments--a comprehensive review of the literature

The Survey of Attitudes Toward Statistics (SATS) was designed for use in both research and instruction. A panel of instructors and introductory statistics students identified by consensus four facets of attitudes toward statistics: (a) Affect-positive and negative feelings concerning statistics; (b) Cognitive Competence-attitudes about intellectual knowledge and skills when applied to statistics; (c) Value-attitudes about the usefulness, relevance, and worth of statistics; and (d) Difficulty-attitudes about the difficulty of statistics as a subject. This structure was validated for a sample of undergraduate students using confirmatory factor analysis. Additional validity evidence was obtained through the correlation of the SATS with Wise's Attitudes Toward Statistics scale, which showed significant, positive relationships between the two instruments.

Ann Del Vecchio

Papers

The development and validation of the Survey of Attitudes toward Statistics