bluuebunny/crossref_metadata_2025_split huggingface dataset:
Matching
Authors: Berger Vance W. , Liu Li , Thach Chau 
Published: September 2005

Abstract
Evaluation is generally inherently comparative. For example, a treatment is generally neither ‘good’ nor ‘bad’ in absolute terms, but rather better than or worse than another treatment. The studies that allow for such comparative evaluations must then also be comparative. To isolate the effects of the treatments under study, avoid confounding treatment effects with unit effects, and possibly increase the power of the study, the units under study must be carefully matched across treatment groups. There are many matching techniques in practice, including crossover designs and case‐control studies. While randomization is often considered to be a competitor to matching, it actually may be considered to be a type of matching, except that here the matching is group‐to‐group.
Mathematical Psychology
Authors: Chechile Richard A. 
Published: September 2005

AbstractMathematical psychology is a branch of theoretical psychology that uses mathematics as a tool for psychological measurement of latent processes as a means to characterize the regular behavioral patterns that emerge from experimental studies. An example of the measurement of latent processes is research that decomposes memory loss into separate components due to storage and retrieval changes. Other models in mathematical psychology provide a detailed description of the results from many memory experiments. Mathematical psychologists have addressed a wide range of psychological problems and have utilized a diverse set of mathematical tools.
Harmonic Mean
Authors: Clark‐Carter David 
Published: September 2005

AbstractThe harmonic mean is a measure of central tendency or location. It is defined as the reciprocal of the mean of the reciprocals of a set of numbers. It plays a role in studies employing between‐subjects designs where the various groups have different sample sizes; in such cases, it produces an appropriate sample size for use in calculations of statistical power and in analysis of variance.
Measurement: Overview
Authors: Michell Joel 
Published: September 2005

AbstractMeasurement in behavioral science includes practices over and above those used in physical and biological science. In part because of this, the understanding of measurement within behavioral science is different from thetraditional concept of measurement. This latter concept is built upon that of a quantitative attribute (i.e., one possessing internal additive structure and sustaining ratios) and it defines measurement as estimation of the ratio between a magnitude of a quantitative attribute and a unit. However, it is not clear whether the attributes specific to behavioral science are quantitative. Consequently, some behavioral scientists favor arepresentational concept of measurement, according to which measurement depends upon isomorphisms or homomorphisms between empirical and numerical systems. This understanding extends the concept of measurement to ordinal and nominal scaling. However, within the framework of the representational concept, the status of one major tradition of quantification in behavioral science, namely psychometrics, is unclear. This has led many behavioral scientists to endorse theoperational concept of measurement, according to which measurement is any precisely specified operation that yields a number. Most behavioral scientists agree that measurement is the assignment of numerals to objects and events according to rules, a definition open to representational and operational interpretations.
History of Intelligence Measurement
Authors: Weidman Nadine 
Published: September 2005

Abstract
The history of intelligence measurement can be roughly divided into three chronological periods: the first (1870–1920s), a period of initial interest in defining intelligence and the establishment and use of intelligence tests; the second (1930s–1960s), a period of repudiation of the hereditarian assumptions underlying the tests; and the third (1969 to present), a period of resurgence of belief in the connection between intelligence and heredity.
Mediation
Authors: Kenny David A. 
Published: September 2005

AbstractA mediator explains, at least in part, a causal relationship between two variables. The total relationship between the two variables is due to a indirect effect, that part explained by the mediator, and due to an direct effect, that part not explained be the mediator. Strong assumptions must be made to estimate and test mediation. The inclusion of multiple variables complicates mediational analysis. Mediational analysis while seemingly simple is actually quite complex.
Meta‐Analysis
Authors: van den Noortgate Wim , Onghena Patrick 
Published: September 2005

AbstractA meta‐analysis combines or compares in a systematic and quantitative way the results from several studies in which a similar research question is investigated. If differences in research outcomes are entirely due to sampling variation, combining the outcomes results in more precise answers to the research questions. In case of systematic inconsistencies in study outcomes, a meta‐analysis offers the possibility to look for an explanation for the heterogeneity, by investigating whether study outcomes are related to one or more study characteristics. In this article, the historical and theoretical context of meta‐analysis and the successive steps in a meta‐analysis are described shortly, and several models, techniques and caveats are discussed.
Mid‐

P

Values
Authors: Berger Vance W. 
Published: September 2005

Abstract

Generally, hypothesis testing is conducted by way of a comparison of the value of a test statistic that summarizes the extent to which the data deviate from the null hypothesis with a reference distribution. The
P
value is the tail area of reference distribution relative to the value of the test statistic. Often, the reference distribution will be discrete, and when it is, the test will be conservative, as only certain
P
values can be attained. This conservatism will result in a loss of power. One approach to dealing with this is the mid‐
P
value, which essentially credits back half of the lost power.
Minimum Spanning Tree
Authors: Michailidis George 
Published: September 2005

AbstractA minimum spanning tree (MST) connects all the vertices of a weighted graph by existing edges whose total weight is minimum. It has been the object of intensive study due to its importance in combinatorial optimization. In this paper, we discuss the formulation of the MST problem, present some of the most commonly used algorithms for its construction, and discuss several of its applications in statistics, probability, and data analysis.
Missing Data
Authors: Little Roderick J. 
Published: September 2005

AbstractMissing data are a common problem in the social and behavioral sciences. Here we present an overview of the problem and possible solutions. We begin by distinguishing between the pattern of missing data and the mechanism that creates the missing data. We then consider common, but limited, approaches: complete‐cases, available cases, weighting analyses, and single imputation. More principled methods, namely, multiple imputation, maximum likelihood, and inference from Bayesian posterior distributions, are then presented.
Model Evaluation
Authors: Navarro Daniel J. , Myung Jay I. 
Published: September 2005

AbstractAssessing the viability of a quantitative model given observed data is generally done in three steps: model fitting, testing, and selection. In model fitting, we find the parameter values that best fit observed data. This is followed by model testing in which null hypothesis significance tests allow us to evaluate the descriptive adequacy of a model's fit to data. Model selection concerns the issue of choosing the model, among a set of competing models, that generalizes best to as yet unseen data samples.
Model Identifiability
Authors: Huang Guan‐Hua 
Published: September 2005

Abstract
A model is identifiable if there is a one‐to‐one correspondence between the probability distribution of the data and the values of model parameters. When applying a nonidentifiable model, different people may draw different conclusions from the same model of the observed data. Before one can meaningfully discuss the estimation of a model, model identifiability must be verified.
Models for Matched Pairs
Authors: Berger Vance W. , Zhang Jialu 
Published: September 2005

Abstract

When randomization is impractical or impossible, matched studies are often used to enhance the comparability of comparison groups, so as to facilitate the assessment of the association between an exposure and an event while minimizing confounding. There are several approaches to the analysis of matched data, including the matched pairs
t
Test, stratified analyses, the McNemar test, and conditional logistic regression. Each approach may be best in a given situation.
Markov ChainMonteCarlo Item Response Theory Estimation
Authors: Keller Lisa A. 
Published: September 2005

AbstractItem response theory (IRT) is the cornerstone of modern measurement. The benefits of using IRT have been well documented. In order to use IRT models, it is necessary to estimate item parameters. As assessments become more complex, more sophisticated models need to be developed. The difficulty in making models more complex is the issue of parameter estimation, and, without the ability to estimate parameters, the models are not useful. With the growth of computer technology, new estimation techniques are being developed for IRT. One such technique is Markov Chain Monte Carlo (MCMC). MCMC provides parameter estimates by simulating draws from the posterior distribution of the parameters, and uses the constructed samples to provide point estimates of the parameters (e.g., means, modes). Both Gibbs sampling and Metropolis–Hastings within Gibbs are described, and examples of estimating parameters of dichotomous IRT models are provided.
M
onte
C
arlo Simulation
Authors: Gentle James E. 
Published: September 2005

Abstract
Monte Carlo simulation is one of the most important tools in all fields of science. Monte Carlo methods use samples of numbers that appear to be random, although the numbers are generated by deterministic computer algorithms. These “pseudorandom” samples can be used in solving a wide range of problems, including both deterministic problems such as evaluating a definite integral and problems in which there is a random component such as studying and comparing the performance of statistical procedures under various assumptions about the underlying distribution.
Multidimensional Item Response Theory Models
Authors: Ackerman Terry A. 
Published: September 2005

AbstractIt is common for educational and psychological tests to be inherently multidimensional, or measuring two or more traits, skills, or dimensions. If assessment data are determined to be multidimensional testing practitioners, researchers can apply multidimensional item response theory (MIRT) to help them better understand what their tests are measuring, how accurately the different composites of ability are being assessed, and use this information to inform the test development process. In this entry, several different aspects of MIRT analyses are discussed: obtaining evidence that the test is multidimensional, estimating the item and ability parameters for a multidimensional model, and illustrating the estimated parameters graphically to gain a better understanding about the composites of traits or skills that are being assessed.
Multidimensional Scaling
Authors: Groenen Patrick J. F. , van de Velden Michel 
Published: September 2005

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.
Multilevel and
SEM
Approaches to Growth Curve Modeling
Authors: Hox Joop , Stoel Reinoud D. 
Published: September 2005

Abstract
Growth curve models are used to model development explicitly as a function of time. Two techniques are commonly used to construct and analyze growth curve models: structural equation modeling, with distinct measurement occasions represented by separate variables, and multilevel regression modeling, with different measurement occasions represented as nested within individuals. Both approaches are essentially the same, but they differ in the way they can be extended into models that are more complex. An example is given, and estimation methods and software are briefly discussed.
Multiple Baseline Designs
Authors: Ferron John , Scott Heather 
Published: September 2005

Abstract
Multiple‐baseline designs are a type of single‐case design used to study treatment effects across multiple participants, multiple behaviors, or multiple settings. Baselines are established by repeated observation. Interventions are then implemented at different times for the different participants, behaviors, or settings. Effects are demonstrated when changes are observed that coincide with the interventions.
Multiple Comparison Procedures
Authors: Keselman H. J. , Holland Burt , Cribbie Robert A. 
Published: September 2005

AbstractMultiple comparison procedures (MCPs) are frequently adopted by applied researchers to locate specific differences between treatment groups. That is, omnibus test statistics, such as the analysis of varianceFtest, can only signify that effects are present, not which specific groups differ from one another (when there are more than two groups). In our paper, we discuss MCPs that can be used to investigate simple pairwise differences between treatment group means, as well as MCPs that can be used to examine complex comparisons (i.e., nonpairwise comparisons) between treatment group means. In particular, we discuss simultaneous as well as stepwise MCPs, emphasizing procedures that can be utilized when the derivational assumptions of the classical procedures of normality and variance homogeneity do not hold.
Multiple Comparison Tests: Nonparametric and Resampling Approaches
Authors: Keselman H. J. , Wilcox Rand R. 
Published: September 2005

Abstract
Resampling‐based multiple comparison procedures attempt to circumvent the deleterious effects of nonnormality and/or variance heterogeneity by utilizing empirical rather than theoretical sampling distributions of test statistics. In particular, the original observed data are randomly resampled in order to build an empirical sampling distribution for the test statistic, and the observed test statistic can then be compared to quantiles in this empirical distribution to determine statistical significance or to compute confidence intervals. We present resampling test statistics that can be used to test pairwise and complex contrasts among treatment group trimmed means. Trimmed means are used to circumvent problems due to nonnormality. Moreover, the test statistic is designed to deal with variance heterogeneity. Combining robust estimators (trimmed means) with a heteroscedastic test statistic (e.g., Welch's (1938) two‐sample test) and applying a bootstrap method results in test statistics that should be robust to the combined effects of nonnormality and variance heterogeneity.
Multitrait–Multimethod Analyses
Authors: Pardy Susan A. , Fabrigar Leandre R. , Visser Penny S. 
Published: September 2005

Abstract
The multitrait‐multimethod (MTMM) framework originally proposed by Campbell and Fiske has long guided efforts to assess the discriminant and convergent validity of measures. Numerous statistical approaches for analyzing MTMM matrices have been proposed. Surprisingly, little consensus has developed on the most appropriate method for analyzing these matrices or what criteria should be used as a basis for choosing the most appropriate method. We provide a nontechnical review of statistical approaches for analyzing MTMM matrices. We conclude with a set of general recommendations regarding the analysis of MTMM data.
Multivariate Analysis:
B
ayesian
Authors: Press Jim 
Published: September 2005

Abstract
This article discusses Bayesian multivariate analysis. It focuses on higher‐dimensional statistical inference based upon the posterior distribution, so inference involves multidimensional prior, posterior, and predictive probability distributions. The article describes credibility intervals and regions, and hypothesis testing. There is reference to both vague and natural conjugate prior distributions. Computer‐intensive methods of numerical evaluation such as MCMC and Metropolis–Hastings algorithms are considered. There is an illustrative example involving Bayesian factor analysis.
Multivariate Analysis: Overview
Authors: Krzanowski Wojtek J. 
Published: September 2005

Abstract
Multivariate analysis is appropriate whenever more than one variable is measured on each sample individual, and overall conclusions about the whole system are sought. Many different multivariate techniques now exist for addressing a variety of objectives. This brief review outlines, in broad terms, some of the more common objectives and sketches out some of the ways in which they are tackled. The techniques are grouped under four headings: visualization and description, extrapolation and inference, discrimination and classification, modeling and explanation. Within each heading, the chief objectives are first identified, and the rationales behind some of the specific techniques are sketched out. The review ends with an outline of current trends and recent developments.
Multivariate Genetic Analysis
Authors: Gillespie Nathan A. , Martin Nicholas G. 
Published: September 2005

Abstract
The goal of multivariate genetic analysis is to estimate the causes of genetic and environmental covariation between phenotypes. As with univariate modeling, when data from genetically informative relatives such as twins are available, the genetic and environmental components of covariation can be separated by a technique which is analogous to factor analysis (Martin & Eaves,). This is achieved by making use of the additional information in the cross‐correlations between relatives for different traits (or same traits measured at different times) and can allow researches to estimate the extent to which genetic and environmental influences are shared in common by several traits or are trait specific. The various forms of multivariate analyses covered in this section include the Cholesky decomposition, common and independent genetic pathway models, and genetic simplex modeling. This section will give the reader an appreciation for the flexibility of SEM approaches to address more complicated questions beyond simpler univariate decompositions.
Multivariate Normality Tests
Authors: Keselman H. J. 
Published: September 2005

AbstractMost classical multivariate procedures (e.g., multivariate analysis of variance, multivariate measures of effect size, classification procedures, maximum likelihood factor analysis) require that the data follow a multivariate normal density function. Behavioral science researchers risk committing many more Type I errors, quantifying inaccurately the magnitude of effect sizes, missing treatment effects, establishing inaccurate confidence intervals, and so on by failing to consider whether their data conform to multivariate normality. This paper discusses a number of options for assessing and dealing with nonnormal multivariate data including: (a) testing for univariate normality among thepmeasures, (b) transforming the data to achieve normality, (c) univariate normal probability plots, (d) multivariate measures of skewness and kurtosis, (e) computing squared distance statistics to locate outlying values, and (f) adopting robust estimators with robust test statistics to circumvent the biasing effects of nonnormality.
Multivariate Outliers
Authors: Fieller Nick 
Published: September 2005

AbstractOutliers in multivariate data provide particular problems primarily because they may not be revealed as an extreme observation in any single component and are, consequently, easy to miss in routine graphical inspection of the data. This article describes some graphical techniques particularly directed towards the objective of detecting outliers before considering more formal methods of testing for discordancy and overcoming the problems of masking and swamping.
Neural Networks
Authors: Murtagh Fionn 
Published: September 2005

AbstractNeural network algorithms attempt to model the way a human reaches decisions for such tasks as categorization and finding associations between observed phenomena. Notwithstanding the prosaic name, they are in fact algorithms with very clear procedures for processing given inputs and targeting particular outputs. We will discuss perhaps the dominant paradigm in this area, referred to as the multilayer perceptron. This class of algorithm is used for categorization, and more generally, iteratively learning an association, or mapping between given inputs and given outputs. The second algorithm we will describe, called theKohonen self‐organizing feature map, attempts to find a simplified map or representation of a set of phenomena. The map gives priority to the finding of association or clustering relationships.
Neuropsychology
Authors: Suchy Yana 
Published: September 2005

Abstract

Neuropsychology is a scientific and clinical discipline that is concerned with understanding the brain‐behavior relationship and the application of this understanding to assessment of patients with brain dysfunction. This chapter surveys statistical applications of both the scientific and the clinical aspects of neuropsychology, including
t
Tests; univariate, factorial, and repeated measures analysis of variance; analysis of covariance; correlational analyses; meta‐analysis; factor analysis; multiple regression; discriminant function analysis; and the receiver operating characteristic curve. Different statistical methods have the ability to contribute to different circumscribed components of clinical neuropsychology practice.
N
eyman–
P
earson Inference
Authors: Nickerson Raymond S. 
Published: September 2005

Abstract
The approach to statistical hypothesis testing developed by Jerzy Neyman and Egon Pearson built upon and extended that of Ronald Fisher, and differed from the latter in several important ways. Ideas that distinguish their approach include choosing between possible hypotheses (in contrast to deciding whether or not to reject a single hypothesis); setting, before experimentation, a criterion for deciding which hypothesis to accept; recognizing the two possible ways to draw an incorrect conclusion from a statistical test and taking account of the fact that they are not likely to be equally desirable; and defining the power of a test as the probability of detecting a real effect if there is one. They saw statistical tests as attempts to quantify commonsense notions about the interpretation of probabilistic data and stressed the importance of the exercise of judgment in their application.
Nonlinear Mixed Effects Models
Authors: Lindstrom Mary J. , Vorperian Houri K. 
Published: September 2005

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).