bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multidimensional Scaling
Authors: F. Groenen Patrick J. , Velden Michel 
Published: September 2014

Abstract
Multidimensional scaling is a statistical technique to visualize dissimilarity data. In multidimensional scaling, objects are represented as points in a usually two dimensional space, such that the distances between the points match the observed dissimilarities as closely as possible. Here, we discuss what kind of data can be used for multidimensional scaling, what the essence of the technique is, how to choose the dimensionality, transformations of the dissimilarities, and some pitfalls to watch out for when using multidimensional scaling.
Structural Equation Modeling: Mixture Models
Authors: Vermunt Jeroen K. , Magidson Jay 
Published: September 2014

Abstract
This article discusses a modeling framework that links two well‐known statistical methods: structural equation modeling (SEM) and latent class or finite mixture modeling. Mixture SEM can either be viewed as a refinement of multivariate normal (MVN) mixtures, where the within‐class covariance matrices are smoothed according to a postulated SEM structure, or as a form of multiple group SEM, in which the group variable is unobserved. After introducing standard MVN mixtures, it is shown how the SEM framework can be used to restrict the class‐specific means and covariances. Furthermore, parameter estimation, model testing, and software is discussed, and an empirical example is provided.
Factor Analysis: Exploratory
Authors: Pruzek Robert 
Published: September 2014

Abstract
Exploratory factor analysis (EFA) provides a way to account for intercorrelations or covariances among all pairs of variables in a battery using a relatively small number of latent variables called common factors. The method can be especially helpful for analyzing structural relationships in situations where observed variables are approximately linearly related to one another, and when individual‐level observations are fallible, that is, subject to errors of measurement. Exploratory factor analysis has gained prominence in many fields over many years, and it has been the subject of much praise as well as criticism. Psychometric and statistical practice has not fully caught up with contemporary appreciations of factor analytic and related methods, but past and recent developments suggest notable promise for continued applications in applied sciences.
Partial Correlation Coefficients
Authors: Brown Bruce L. , Hendrix Suzanne B. 
Published: September 2014

Abstract
Partial correlation is a method for identifying the correlation between two variables, with the effects of a third variable held constant. It is mathematically equivalent to the correlation between the residual scores of the two variables regressed upon the control variable. Partial correlation is conceptually related to, and an integral part of, a number of other useful statistical methods, including path analysis, structural equation modeling, factor analysis, and stepwise multiple regression.
Reproduced Matrix
Authors: Lance Charles E. 
Published: September 2014

Abstract
A reproduced matrix is a matrix of correlations or covariances that is calculated from parameter estimats obtained for a path analytic, factor analytic, or latent variable structural equation model. If one or more ‘zero restrictions’ are specified, model goodness‐of‐fit can be evaluated by the extent to whihc the reproduced matrix equals (or approximates) the observed matrix of correlations or covariances calculated directly from the data, for exdample, using the chi‐square statistic.
Scaling Asymmetric Matrices
Authors: Takane Yoshio 
Published: September 2014

Abstract
Square tables in which some of the elements located symmetrically about the diagonal are not equal to each other are called asymmetric tables. Asymmetric tables arise in a variety of different ways. They can be roughly classified into two groups, one arising from measurements of antisymmetric relationships, and the other, of proximity relationships. A variety of models have been proposed to capture the asymmetry in such tables. In this article, we briefly discuss some of the representative models of asymmetry for each of the two types of data.
Factor Analysis: Confirmatory
Authors: Byrne Barbara M. 
Published: September 2014

Abstract
The intent of this article is to present an introductory overview of the basic concepts and testing procedures associated with confirmatory factor analysis (CFA). Working from a hypothesized four‐factor structure of a nonacademic self‐concept (SC), as measured by 32 items from the Self Description Questionnaire, readers are introduced to both a graphical and a structural equation specification of this CFA model, followed by a review of the related procedures involved in its testing, estimation, and evaluation, together with the interpretation of results. Important issues and caveats associated with the testing of CFA models are also briefly addressed.
Analysis of Covariance: Nonparametric
Authors: Berger Vance W. 
Published: September 2014

Abstract
There are numerous benefits in adjusting an analysis for one or several covariates, but often these benefits come at the price of having to make additional assumptions, such as distributional assumptions or assumptions regarding the relationship between a covariate and a response variable. For example, the validity of the popular analysis of covariance model is predicated on a variety of assumptions. Clearly, the need to make assumptions, or to restrict the results so that they apply only when these assumptions are true, is a weakness, and it would be preferable to reap the benefits of covariate adjustment without having to rely on unreasonable assumptions. There is a body of nonparametric covariate adjustment techniques that aim to allow for this. Specific techniques depend on the nature of the covariate (e.g., binary or continuous).
S
pearman's Rho
Authors: Kornbrot Diana 
Published: September 2014

Abstract

Spearman's rho,
r

s

, is a correlation measure of association based on ranks of scores rather than raw scores. It is applicable if either or both variables are direct rankings or only ordinal. It is also applicable when both variables are metric but not normally distributed, or when the relation between the variables is monotonic but nonlinear.
Wilcoxon‐Mann‐Whitney Test: Overview
Authors: DePuy Venita , Berger Vance W. , Zhou YanYan 
Published: September 2014

Abstract

The Wilcoxon‐Mann‐Whitney test evaluates the difference in medians between two similarly shaped populations, which have the same variance. This nonparametric test is similar to the two‐sample Student's
t
Test. The primary differences are that the
t
Test requires the assumption of normality, while the Wilcoxon‐Mann‐Whitney test can be performed where only rankings, that is, ordinal data, are recorded. While exact computations of the test statistic are possible, a large sample approximation is often used for ease of calculation. Both methods are available in various commercial statistical packages.
Polychoric Correlation
Authors: Hershberger Scott L. 
Published: September 2014

Abstract

The polychoric correlation is used to estimate the Pearson product‐moment correlation between two continuous, bivariate‐normally distributed variables from categorized versions of those variables. The polychoric correlation may also be thought of as the correlation between the normal scores
Y

i

*,
Y

j

* as estimated from the category scores
Y

i

,
Y

j

. These normal scores are based on estimated threshold values that represent transitions from one category to the other.
Paired Observations, Distribution Free Methods
Authors: Berger Vance W. , Li Zhen 
Published: September 2014

AbstractData are said to be paired if theith observation on the first sample is naturally paired with theith observation on the second sample. The objective in paired observations is to eliminate, to the extent possible, sources of extraneous variation. In behavioral studies, for example, paired observations may be employed to assess the effectiveness of a treatment or experimental procedure. Techniques often used to analyze paired observations include the McNemar Chi‐square test, the pairedtTest, the sign test, and the Wilcoxon Signed‐Rank test.
Randomized Block Design: Nonparametric Analyses
Authors: Liu Li , Berger Vance W. 
Published: September 2014

Abstract
In a randomized block design, there are, in addition to the experimental factor or factors of interest, one or more nuisance factors. The role of blocking is to reduce or eliminate that part of the experimental error attributable to these nuisance factors. Standard parametric ANOVA can be used to analyze such designs, but it requires the normality assumption, and may be misleading if the errors are not normally distributed or if there are outliers. In this article, we consider some distribution‐free tests, such as the sign test, the Wilcoxon signed rank test, Friedman's test, aligned rank tests, Durbin's test, and the row mean score test. None of these methods require the normality assumption, and they can all be used to analyze such designs.
One Way Designs: Nonparametric and Resampling Approaches
Authors: Hayes Andrew F. 
Published: September 2014

Abstract
Resampling‐based variants of the one‐way analysis of variance (ANOVA) are discussed, including their assumptions, computation, and interpretation. Although resampling approaches to comparing groups offer some appealing advantages to ANOVA, they are not a panacea to assumption violations. Caution must be exercised in the interpretation of a resampling version of ANOVA because the null hypothesis tested depends on the test statistic and the method of resampling used.
Generalized Linear Models: Introduction
Authors: Everitt Brian S. 
Published: September 2014

AbstractGeneralized linear models provide a unified framework for regression models including multiple regression, logistic regression, analysis of variance, and analysis of covariance. Such models consist of three components – a link function specifying the transformation of the response variable to be modeled by a linear function of the explanatory variables, an error distribution suitable for each type of response, and a variance function specifying the relationship between the mean and variance of the error distribution.
Analysis of Variance: Multiple Regression Approaches
Authors: Harris Richard J. 
Published: September 2014

Abstract

One‐way and factorial independent‐means analysis of variance (ANOVA) can be accomplished via a multiple regression analysis (MRA) in which the dependent variable is predicted from sets of level‐membership variables or, preferably, contrast variables representing the factors. Doing so can be useful in the two‐group design in that it provides an easily interpretable and often remarkably low estimate of the proportion of variance in the dependent variable, that is accounted for by an experimental manipulation. It also helps in the choice among alternative approaches to analyzing unequal‐
n
factorial designs by showing that full‐model analyses involve the comparison of unweighted means and that testing an effect uncorrected for any other effects implies making comparisons among weighted means. Furthermore, testing an effect corrected for confounds with some, but not all, other effects involves comparisons among the means that are difficult to characterize, require hand computations beyond those provided by ANOVA computer programs, and are unlikely to be of any real interest to the researcher. SPSS commands for carrying out factorial ANOVA via the MANOVA and REGRESSION commands are provided.
Nonlinear Mixed Effects Models
Authors: Lindstrom Mary J. , Vorperian Houri K. 
Published: September 2014

Abstract
Nonlinear mixed‐effects (NLME) models are a generalization of LME and nonlinear regression models and are used when data have a hierarchical growth‐curve structure where an LME model might be used but a nonlinear model is required to fit the data. Both LME and NLME models require some care in estimating and testing model parameters. NLME models have additional difficulties due to the lack of a closed form for the marginal distribution of the data (see following sections).
Intraclass Correlation
Authors: Field Andy P. 
Published: September 2014

Abstract
Intraclass correlations are used to assess the consistency between measures of the same class (i.e., measures of the same thing). Two common uses are in comparing paired data (such as twins) on the same measure and assessing the consistency between judges' ratings of a set of objects. There are different conceptualizations of the intraclass correlation and the variance components used to calculate them in these different models. There is an important distinction between the cases when the measures are a single score or an average of multiple scores, and when a measure of consistency or a measure of absolute agreement is required.
Regression Model Coding for the Analysis of Variance
Authors: Cohen Patricia 
Published: September 2014

Abstract
Analyses that are typically carried out by analysis of variance (ANOVA) can often be carried out with multiple regression (MR) methods as well. The MR approach can include direct tests of the study's specific hypotheses and cope efficiently with unbalanced designs. The methods of coding groups for MR analysis should depend on the study design and hypotheses. Alternative methods are illustrated and discussed.
Population Stratification
Authors: Oord Edwin J. C. G. 
Published: September 2014

Abstract
In case‐control studies, population stratification can cause associations between marker alleles and disease status. These associations are ‘spurious’ because the marker is not informative about the location of a disease mutation. Family‐based tests are an important tool to avoid such spurious findings but the extent to which these tests are really needed is a topic of debate.
Dropouts in Longitudinal Data
Authors: Leeuw Edith D. 
Published: September 2014

Abstract
Different types of nonresponse threaten the validity of longitudinal studies: First, the initial nonresponse during the recruitment in the base‐line survey; Second, successive dropout at each time point. In this entry, specific attention is given to dropout and strategies to limit the problem. Dropout in longitudinal surveys has three separate sources: failure to locate research participants, failure to contact research participants, and failure to achieve cooperation. Effective measures are described to reduce nonresponse. To limit nonresponse, a total design approach is advocated with specific attention to each source. That is to limit both noncontact (i.e., failure to locate and subsequent failure to contact a located research participant), and noncooperation.
Attrition
Authors: Shadish William R. , Luellen Jason K. 
Published: September 2014

Abstract
Attrition refers to a loss of response from study participants. Random attrition reduces statistical power but does not bias the results of a study. Systematic attrition reduces statistical power and can lead to generalizability biases and causal inference biases. For these reasons, it is best to prevent or minimize attrition. Attrition from measurement can and should be avoided even when attrition from treatment cannot be avoided. For attrition that cannot be prevented, a number of alternative strategies attempt to account for attrition during data analysis.
Randomization: Overview
Authors: Boik Robert J. 
Published: September 2014

AbstractBehavioral scientists were among the earliest to use randomization in research. Justifications for using randomization are plentiful and include the following: it ensures that estimators are unbiased and consistent; it ensures that permutation tests and associated confidence intervals are valid; it justifies normal‐theory tests, even in the absence of normality; it protects against subjective biases of the investigator; it helps to elucidate causality; and it enhances the robustness of inferences. Nonetheless, randomization is not universally accepted. Randomization has been criticized because it violates the conditionality and likelihood principles and because it can be less efficient than methods based on optimal sampling and optimal design.
Sequential Testing
Authors: Coad D. S. 
Published: September 2014

Abstract
The use of sequential methods in clinical trials dates back to the 1940s. With these methods, the accruing data are inspected periodically in order to assess whether the trial should be continued. Trials can be stopped when there is convincing evidence of a treatment effect or they can be stopped for futility. The use of sequential methods can reduce the numbers of patients in the trial compared with a design with a fixed sample size.
Placebo Effect
Authors: Onghena Patrick 
Published: September 2014

Abstract
A placebo effect is conceptually defined as the beneficial effect associated with an intervention that does not include the presumed active ingredients. It can be operationally defined as the positive difference between the results of a placebo condition and a no‐treatment condition in a randomized trial. Placebos play a crucial role as a control in clinical medication trials, but discussion remains regarding their effect sizes and possible working mechanisms. As a confounding factor, a placebo effect is considered as a specific type of reactivity effect that should be distinguished from other confounding factors like history, spontaneous recovery, or statistical regression.
Prevalence
Authors: Grietens Hans 
Published: September 2014

AbstractPrevalence is determined as the proportion of individuals in a population who have a specified disease. Depending on the period of time that is taken into account, point, period, or lifetime prevalences can be calculated. Prevalence studies are part of epidemiological research. They are conducted in medicine, psychiatry, and behavioral science. They are useful to determine the needs for services in a certain area and allow the study of variations of outcomes over time and place. There is a dynamic relationship between prevalence and incidence. Prevalence rates are a function of both the incidence and the duration of a disease.
Common Pathway Model
Authors: Rijsdijk Frühling 
Published: September 2014

Abstract

While traditional multivariate statistical methods can describe correlational patterns of a set of variables (e.g., psychiatric symptoms or problem behaviors in children), they cannot provide insight into why certain symptoms or behaviors tend to co‐occur in a population. This can be achieved using methods of
multivariate genetic analysis
. In multivariate genetic models, two main classes can be distinguished, which differ in the way the common factors are assumed to influence the different observed variables. The way unique factors influence the observed variables is the same in both classes. The first class of models is referred to as common pathway or psychometric models. Common genetic and environmental factors influence all observed variables via a single psychometric factor, or underlying latent liability. This factor is identical to the factor derived from higher order phenotypic factor analyses, with the difference that twin data allows estimation of the relative importance of genetic and environmental effects of this factor. The second class of models is referred to as
independent pathway models
or biometric models, where the common genetic and environmental factors influence the observed variables directly, without an intermediate higher order factor. As a result, the common genetic and environmental factors do not necessarily cause similar groupings of variables.
Clustered Data
Authors: Fitzmaurice Garrett M. 
Published: September 2014

Abstract
Clustered data commonly arise in studies in the behavioral sciences. For example, clustered data arise when observations are obtained on children nested within schools. From a statistical standpoint, the main feature of clustered data that needs to be accounted for in any analysis is the fact that units from the same cluster are likely to respond in a more similar manner than units from different clusters. As a consequence, clustered data may not contain quite as much information as investigators might otherwise like to believe it does.
Nonshared Environment
Authors: Towers Hilary , Neiderhiser Jenae M. 
Published: September 2014

Abstract
Findings from decades of research show that the environmental experiences of individuals reared in the same home can make them different from one another as well as similar. This is true for a wide range of individual characteristics and personal relationships. The phenomenon in which environmental influences cause family members to differ from one another is known in the behavioral genetic literature as nonshared environment.
Family History Versus Family Study Methods in Genetics
Authors: Burke Ailbhe , McGuffin Peter 
Published: September 2014

Abstract
Family history methods, where the familiality of a trait or disorder is explored by taking a history from one or more informants, are less costly and time‐consuming than family studies where a direct interview and examination of all available relatives of an index case is carried out. However family studies have a higher sensitivity. Studies based on families alone cannot distinguish between the effects of shared genes and shared environment.
Clinical Psychology
Authors: Treat Teresa A. , Weersing V. Robin 
Published: September 2014

AbstractRapid advancement in the understanding and amelioration of complex clinical phenomena places heavy demands on clinical scientists for thoughtful articulation of theoretical models, methodological expertise, and statistical rigor. This article provides a descriptive and prescriptive overview of the use of statistics in clinical psychology. Five classes of research questions that clinical psychologists commonly address are described: the definition and measurement of constructs, the evaluation of group differences, the prediction of group membership, the examination of a network of constructs, and the evaluation of research questions across studies or data sets. A set of four principles that underlie the use of statistics in clinical research are described: the importance of theory to the rapid advancement of clinical science; the centrality of model specification, evaluation, and comparison to clinical research; the need for researchers to master research design and the mechanics of statistical techniques; and the limitations of null‐hypothesis statistical testing (NHST).
Inbred Strain Study
Authors: Wade Claire 
Published: September 2014

Abstract
Genetic contributions in complex trait analyses can be controlled or removed by the use of inbred strains as the test subjects. To be regarded as inbred strains, the mammals concerned must have been subjected to at least 20 generations of full‐sib mating (the expected inbreeding coefficient will be 98.6% or greater). Variation in the test scores observed within inbred strains will be of either environmental (treatment) origin or otherwise caused by random experimental error, since genetic variation is largely absent.
Quantitative Methods in Personality Research
Authors: Fraley R. Chris , Marks Michael J. 
Published: September 2014

Abstract
The science of personality documents and explains ways that people differ with respect to their thoughts, feelings, social relationships, and experiences. Quantitative methods such as factor analysis played an important role in facilitating the discovery of the fundamental dimensions of personality. Recent developments in the application of multiple regression, structural equation modeling, hierarchical linear modeling, item response theory, and taxometrics allow personality researchers to test increasingly sophisticated models of personality processes.