bluuebunny/crossref_metadata_2025_split huggingface dataset:
Projection Pursuit
Authors: Stuetzle Werner 
Published: September 2005

AbstractProjection pursuit was conceived as a method for finding ‘interesting’ one‐ or two‐dimensional projections of multivariate data revealing unanticipated structure such as clusters, skewness, or presence of outliers. The initial idea of exploring high‐dimensional data using low‐dimensional marginals was later extended into a general paradigm for modeling multivariate data that has been applied to density estimation, regression, and classification.
Propensity Score
Authors: D'Agostino Ralph B. 
Published: September 2005

Abstract
In observational studies, treatments cannot be randomly assigned. The treated and nontreated (i.e., control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. The propensity score, defined as the conditional probability of being treated given the observed covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator, given the observed covariates. Once estimated, the propensity score can be used to reduce bias through matching, subclassification, regression adjustment, or some combination of all three. In this entry, we briefly discuss the uses of propensity score methods for bias reduction.
Proximity Measures
Authors: Bock Hans‐Hermann 
Published: September 2005

AbstractProximities describe the similarity or dissimilarity between items or objects in a numerical way. General properties and special definitions are presented. Proximities are typically used for analyzing and visualizing hidden similarity structures by graphical displays or when looking for homogeneous clusters in a set of data points. Various types of dissimilarity measures are uniquely related to special types of clusterings or visualizations. Finally, we characterize the proximity between probability distributions by various divergence measures.
Psychophysical Scaling
Authors: Irtel Hans 
Published: September 2005

Abstract
Psychophysical scaling investigates methods for mapping physical stimulus properties into sensation scales. The two major types of subject tasks used are either based on discriminability of rather similar stimulus intensities, or on direct judgments about perceived differences of clearly distinct stimuli. Classical discrimination scaling tries to find a stimulus transformation such that equally discriminable stimuli are mapped onto constant differences on the sensation scale. Direct scaling methods include operations on the set of stimuli that represent certain numerical ratios of perceived stimulus intensity, or require the subject to actually assign these numerical ratios to given pairs of stimuli. In many cases, the power law is an acceptable description of the empirical data.
Quantitative Methods in Personality Research
Authors: Fraley R. Chris , Marks Michael J. 
Published: September 2005

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.
Evaluation Research
Authors: Lipsey Mark W. , Tidd Simon T. 
Published: September 2005

AbstractQuantitative methods are widely used in the evaluation of social programs. The most methodologically challenging type of evaluation attempts to determine if a program actually has the intended effects on the social conditions it addresses. Variations on experimental and quasi‐experimental research designs are used for this purpose, but their application to social programs involves many distinctive issues, problems, and pitfalls.
Quasi‐experimental Designs
Authors: Shadish William R. , Luellen Jason K. 
Published: September 2005

Abstract
Quasi‐experiments are used to test hypotheses about the effects of manipulable treatments but lack the process of random assignment that occurs with true experiments. Consequently, researchers must be particularly concerned with reducing threats to the validity of causal inferences. Quasi‐experiments can be thought of as a collection of design elements aimed at reducing threats to validity. A number of threats to validity and basic quasi‐experimental designs are discussed.
Quasi‐Symmetry in Contingency Tables
Authors: Hershberger Scott L. 
Published: September 2005

Abstract
Symmetry models require that corresponding cells above and below the main diagonal of a square contingency table be equal, a requirement that is rarely met because symmetry models also require marginal homogeneity. A less restrictive model, quasi‐symmetry, permits marginal heterogeneity while requiring equal odds ratios above and below the main diagonal. Ordinal quasi‐symmetry models specify a parameter that represents a constant difference between adjacent levels of the response categories.
R
‐Squared, Adjusted
R
‐Squared
Authors: Miles Jeremy 
Published: September 2005

Abstract

R
‐squared and adjusted
R
‐squared are statistics derived from analyses based on the general linear model (e.g., regression, ANOVA). It represents the proportion of variance in the outcome variable which is explained by the predictor variables in the sample (
R
‐squared) and an estimate in the population (adjusted
R
‐squared).
Random Effects and Fixed Effects Fallacy
Authors: Smith Philip T. 
Published: September 2005

Abstract

The distinction is made between fixed effects and random effects and applied to the analysis of experiments involving linguistic materials, where its use has become a contentious issue. Specifically, some researchers have argued that treating linguistic variables such as words as fixed effects in analysis of variance is fallacious. Methods for analyzing data containing two random‐effect factors are reviewed, and the conclusion reached that the use of quasi‐
F
ratios or their approximation by min
F
′ is often best practice. Alternative legitimate analyses are also discussed.
Random Forests
Authors: Cutler Adele 
Published: September 2005

Abstract
Random forests are collections of trees that give accurate predictions for regression, classification, and clustering problems. They handle missing values, can be used for continuous and/or categorical input variables, and allow class weighting to adjust for unequal sampling schemes. Random forests do not overfit the data so they can be used for problems in which the number of input variables is much larger than the number of observations. Random forests provide measures of variable importance and proximities that can be used to interpret the data.
Randomization
Authors: Boik Robert J. 
Published: September 2005

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.
Randomized Block Design: Nonparametric Analyses
Authors: Liu Li , Berger Vance W. 
Published: September 2005

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.
Rater Agreement
Authors: Schuster Christof , Smith David 
Published: September 2005

Abstract
Human judgment is prone to error that researchers routinely seek to quantify, understand, and minimize. To quantify the quality of nominal judgments, agreement among multiple raters of the same target is examined using either global summary indices, such as Cohen's kappa, or by modeling important properties of the judgment process using latent class analysis. In this entry, we give an overview of the latent class analysis approach to nominal scale rater agreement data.
Rater Bias Models
Authors: Saudino Kimberly J. 
Published: September 2005

Abstract
Raters' response tendencies, standards, or expectations can result in the tendency to consistently overestimate or underestimate the behavior of targets. Such rater biases can influence the outcomes of behavioral genetic studies. Quantitative genetic models allow researchers to determine the significance of rater biases and estimate genetic and environmental influences on behaviors controlling for bias.
Mendelian Genetics Rediscovered
Authors: Dickins David 
Published: September 2005

AbstractThree European botanists coincidentally claimed their independent ‘rediscovery’ in 1900 of Mendel's neglected 1865 paper, in which he derived what are still the classic principles of genetics, from breeding experiments with peas in his monastery garden. It is necessary to get a clearer picture of why Mendel was doing what he did, and how the ‘rediscoverers’, and others enthused by them, misinterpreted its significance in some important respects, leading to an eclipse of Darwinism, not to recover for a further two or three decades of dispute and reinterpretation, after which the complementarity of Mendel's work with Darwin's became established.
Regression Discontinuity Design
Authors: Shadish William R. , Luellen Jason K. 
Published: September 2005

Abstract
The regression discontinuity design is a quasi‐experimental design in which the experimenter assigns participants to treatment conditions on the basis of a cutoff score on an assignment variable – usually a measure of need or merit. This design yields unbiased estimates of treatment effects when certain assumptions are met. Just as in a randomized experiment, the selection process is both completely known and perfectly measured in the regression discontinuity design. Numerous variations of the design are possible, including combinations with randomized experiments.
Regression Model Coding for the Analysis of Variance
Authors: Cohen Patricia 
Published: September 2005

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.
Repeated Measures Analysis of Variance
Authors: Everitt Brian S. 
Published: September 2005

Abstract
Repeated measures data arise when each participant in a study has measurements of a response variable of interest made under several different conditions, on different occasions, and so on. A univariate analysis of variance approach can be used if the covariance structure of the repeated measurements satisfies a condition known as sphericity, otherwise a multivariate analysis of variance approach may be needed.
Reproduced Matrix
Authors: Lance Charles E. 
Published: September 2005

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.
Resentful Demoralization
Authors: Onghena Patrick 
Published: September 2005

Abstract
Resentful demoralization is a threat to the construct validity of the treatment in intervention studies. This threat may occur in intervention studies in which comparison groups not obtaining a desirable treatment become discouraged or retaliatory and, as a result, perform worse on the outcome measures. Some strategies to avoid, minimize, or deal with resentful demoralization are suggested and illustrated.
Residual Plot
Authors: Miles Jeremy 
Published: September 2005

Abstract
In a regression analysis, a number of assumptions about the data must be made and tested. One of the most powerful ways to do this is to produce a range of graphs of the residuals from the regression. Valuable information may be gained from examining a histogram of the residuals, a plot of the residuals against time, and a plot of the residuals against the predicted values from the regression equation.
Retrospective Studies
Authors: McKenzie Dean P. 
Published: September 2005

Abstract
A retrospective study is an observational study that deals with events that have occurred in the past. Information may be obtained from historical records or by asking individuals to remember previous experiences or exposures. Case‐control studies, in which previous exposures of those individuals with (cases) and without (controls) a particular outcome or disease are examined, are generally retrospective. Historical cohort studies are also retrospective. Cohort members are identified on the basis of past characteristics, and then studied from a previous time‐point up to the present. Retrospective studies are often used when prospective cohort studies (where cohorts are selected on the basis of current characteristics and followed into the future) would be impractical, for example, if the event of interest takes a long time to occur.
Risk Perception
Authors: Everitt Brian S. 
Published: September 2005

Abstract
Quantifying and assessing risk involves the calculation and comparison of probabilities. Research shows that people tend to overestimate the probability of unfamiliar, catastrophic, and well‐publicized events and to underestimate the probability of unspectacular or familiar events that claim one victim at a time. The ability to assess risk in a rational manner would benefit both individuals and society, but there is considerable evidence that persuading people to be rational about risk is not easy.
Robust Testing Procedures
Authors: Wilcox Rand R. 
Published: September 2005

Abstract
Robust testing procedures are hypothesis testing techniques that are relatively insensitive to violations of assumptions such as normality and homoscedasticity. Major advances on how to study and develop robust testing techniques have appeared during the last forty years. The result has been a deeper understanding of why conventional ANOVA and regression methods can be unsatisfactory, plus a collection of new methods that have practical value.
Robustness of Standard Tests
Authors: Wilcox Rand R. 
Published: September 2005

Abstract

Conventional hypothesis‐testing methods such as student's
t
, the ANOVA
F
test, and methods based on the ordinary least squares regression estimator, are not robust to violations of assumptions. In fact, there are general conditions under which these methods can provide poor control over the probability of a type I error and inaccurate confidence intervals, no matter how large the sample sizes might be. Relatively poor power is yet another concern.
Runs Test
Authors: Lunneborg Clifford E. 
Published: September 2005

Abstract
The Wald–Wolfowitz runs test dates from 1940, making it one of the earliest nonparametric tests. It provides a test of a common distribution for two independent random samples. However, the test has low power relative to such alternatives as the Kolmogorov–Smirnov or Cramér–von Mises two‐sample tests and has declined in popularity.
Sampling Issues in Categorical Data
Authors: Hershberger Scott L. 
Published: September 2005

Abstract
The sampling method used to collect response on categorical variables determines both the probability of the specific configuration of a contingency table, and, more importantly, determines the interpretation of the data. Sampling methods leading to three probability distributions; the hypergeometric, the binomial, and the negative binomial, are discussed. The three sampling methods indicate the same probability of observing an example contingency table, but lead to different interpretations of the data.
Saturated Model
Authors: Hershberger Scott L. 
Published: September 2005

Abstract
A saturated model is a model that perfectly fits the data because it has as many parameters as there are values to be fitted. Saturated models are also just‐identified models, and can provide useful information in the search for a more parsimonious, unsaturated model in which some of the parameters of the saturated are not estimated. Saturated and unsaturated models must be hierarchically nested in order to evaluate the unsaturated model's fit to the data.
Scales of Measurement
Authors: Wuensch Karl L. 
Published: September 2005

Abstract
Scales of measurement, as conceived by S. S. Stevens, are defined with respect to (1) the extent to which empirical relationships among the measured objects correspond to numerical relationships among the measurements, and (2) the types of transformations that can be applied to the scale without distorting the structure of the scale. Stevens argued that whether or not a particular statistical analysis is ‘permissible’ depends upon the scale of measurement of the data being analyzed. While this assertion has been defended by some statisticians, it has been attacked by others. Many statistical textbooks for social scientists are organized around the assertion that scale of measurement is a critical factor in determining which statistical analysis is appropriate. Here, the authors argue that parametric statistical methods are appropriate only for interval level data and that nonparametric methods should be employed with noninterval data. One's attitude about the relationship between scale of measurement and choice of statistical analysis may follow from one's more basic ideas about measurement and the nature of reality. Ultimately, the distinction between interval and ordinal scales depends on whether one believes the relationship between the measurements and the true amounts of the measured attribute is positive linear (interval) or merely positive monotonic (ordinal), and such beliefs are largely a matter of faith.
Scaling Asymmetric Matrices
Authors: Takane Yoshio 
Published: September 2005

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.
Scaling of Preferential Choice
Authors: Böckenholt Ulf 
Published: September 2005

Abstract
Scaling models for preferential choice data serve the dual purpose to summarize choice data and to facilitate the forecasting of choices by decision makers facing possibly new or different variants of the choice options. The summary representation obtained by scaling methods can provide useful information for identifying both option characteristics that influence choice and systematic sources of individual differences in the evaluation of these option characteristics. Many scaling models are based on Thurstone's random‐utility approach and allow the analysis of different types of both revealed and stated choice data.
Second Order Factor Analysis: Confirmatory
Authors: Tisak John , Tisak Marie S. 
Published: September 2005

Abstract
Confirmatory second‐order factor analysis may be conceptualized as a further factor analyzing of the results of an initial factor analysis of some observed variables. Specifically, the initial or first‐order factor means, variances, and covariances are re‐analyzed into more general or abstracted variables. For pedagogical reasons, this approached is initially developed via a first and second‐order multivariate multiple regression analysis using measured or observed variables. Next by analogy, the approach is extended to latent variates or unobserved factors. Throughout an illustration is presented using Thurstone's Primary Mental Abilities, Horn and Cattell's Fluid and Crystallized Intelligences, and Spearmen's General Intelligence.
Selection Study (Mouse Genetics)
Authors: Henderson Norman 
Published: September 2005

Abstract
Artificial selection for the high and low expression of behavioral traits has long been successful. The apparent simplicity of selection experiments belies the fact that unless the effective breeding size of each parental generation is very large, genetic drift can add complications to genetic interpretations. Without replicate lines to estimate genetic variance due to random drift, the effects of drift on heritability estimates and as a possible source of spurious associations between the selected trait and other traits cannot be readily assessed.
Structural Equation Modeling: Checking Substantive Plausibility
Authors: Jaffe Adi , Hershberger Scott L. 
Published: September 2005

Abstract
The process of construct validation involves a series of three steps, namely, concept definition, concept operationalization, and empirical confirmation. These steps lead to specifying and confirming a nomological network for the construct to be validated. Structural equation modeling efficiently confirms the substantive plausibility of the nomological network.
Linear Statistical Models for Causation: A Critical Review
Authors: Freedman D. A. 
Published: September 2005

AbstractWe review the basis for inferring causation by means of linear statistical models. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions that must be satisfied. Stability is difficult to establisha priori, and the statistical conditions are equally problematic. Therefore, causal relationships are seldom to be inferred from a data set by running regressions, unless there is substantial prior knowledge about the mechanisms that generated the data.