bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ageing Models
Authors: Müller Hans‐Georg 
Published: September 2014

AbstractModels for aging have come under intensive scrutiny in recent years due to increasing interest in aging, not least due to the ever‐increasing human lifespan. The methods used to model aging are largely similar to those in survival analysis, with unique features such as the use of lifetables and less prominence of incomplete data. An increasingly prominent role is played by event history analysis. Nonparametric, frailty, event history, and extreme value approaches are increasingly prominent due to the uncertainties regarding the shape of population hazard functions, which display remarkable plasticity across sexes, species, and environmental conditions. These newer approaches complement classical likelihood methods associated with the Gompertz and Gompertz–Makeham parametric models and are therefore the methods of choice in the emerging field of biodemography. Recent problems where aging models are relevant concern the influence of genetic variants and signaling pathways on mortality, the modeling of plateaus in the right tail of hazard functions, and the complex interplay between reproduction, diet, and mortality.
Bayesian Methods for Categorical Data
Authors: Gutiérrez‐Peña Eduardo 
Published: September 2014

AbstractThe Bayesian approach to statistical inference is based on a subjective interpretation of probability. Given a prior distribution describing our prior beliefs concerning the value of an unknown quantity, Bayes' theorem allows us to update those beliefs in the light of observed data. The resulting posterior distribution then summarizes all the available information about the quantity of interest, conditional on the posited model. Bayesian methods for the analysis of categorical data use the same classes of models as the classical approach. However, Bayesian analyses can be more informative – the posterior distribution of the parameter contains more information than mere point estimates or test statistics – and may provide more natural solutions in certain situations such as those involving sparse or missing data, or unidentifiable parameters.
Number of Matches and Magnitude of Correlation
Authors: Rovine Michael J. 
Published: September 2014

AbstractA definition of the correlation as the proportion of matches between two continuous variables is described. This definition is compared to other coefficients; in particular, coefficients that are used to describe the association in a 2 × 2 contingency. These include Yule's Q, Peirce's coefficient, the φ‐coefficient, and the Binomial Effect Size Display (BESD).
Power Analysis for Categorical Methods
Authors: Erdfelder Edgar , Faul Franz , Buchner Axel 
Published: September 2014

Abstract

This article shows how to assess and control the power of chi‐square tests for categorical data. It discusses both the case of simple null hypotheses (i.e., the category or cell probabilities are completely specified
a priori
) and the case of composite null hypotheses for parameterized multinomial models requiring parameter estimation (e.g., log‐linear models). Power calculations are illustrated for chi‐square tests of association and chi‐square goodness‐of‐fit tests using standard examples from behavioral research.
Tetrachoric Correlation
Authors: Hershberger Scott L. 
Published: September 2014

Abstract
The tetrachoric correlation is used to estimate the Pearson product‐moment correlation between two continuous, bivariate‐normally distributed variables from dichotomized versions of those variables. Two common areas of application of the tetrachoric correlation are to the behavior genetic analysis of twin data and to the factor analysis of item data from cognitive ability tests.
Randomization Based Tests
Authors: Ludbrook John 
Published: September 2014

Abstract
I argue that when an experiment in behavioral psychology is designed, it almost always involves taking a nonrandom sample and, by a process of randomization, dividing it into two or more ‘treatment’ groups. This places such experiments within the randomization model of inference, not the better‐known population model. The appropriate tests of significance under the randomization model are permutation (randomization) tests.
Confounding Variable
Authors: Onghena Patrick , Noortgate Wim 
Published: September 2014

Abstract
A confounding variable is a variable that is related to an explanatory variable and at the same time has an effect on a response variable that is entangled with a possible effect of the explanatory variable, possibly leading to wrong conclusions about the latter. Confounding may also occur in situations involving more than one explanatory or response variable, and more than one confounding variable may be present. Observational studies, quasi‐experiments, as well as randomized experiments might be affected by confounding. If confounding variables cannot be avoided or ruled out by design, a final option is to model them as covariates.
Multiple Baseline Designs
Authors: Ferron John , Scott Heather 
Published: September 2014

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.
Binomial Distribution: Estimating and Testing Parameters
Authors: Berger Vance W. , Zhou YanYan 
Published: September 2014

Abstract
We consider categorical variables with only two possible values. Such data are referred to as binary data, Bernoulli variables, dichotomous data, and sometimes as 0/1 data. In contrast with continuous data, which are generally summarized with sums and averages, binary data are generally summarized with counts and proportions. The binomial distribution is generally used to model the proportion of binary trials that result in a given outcome. When testing a binomial population to determine if the success probability appears to be a given specified value, the binomial test is used.
Laws of Large Numbers: Applications
Authors: Kay Jim W. 
Published: September 2014

Abstract

The laws of large numbers concern when the average of
n
random variables converges to a fixed value as
n
tends to infinity. The
n
random variables could arise in various ways. They could, for example, be mutually independent and identically distributed and represent a simple random sample of observations taken from some fixed population, or they could represent a sequence of
n
realizations of a stationary stochastic process. The laws are defined under the assumptions that the random variables are mutually independent and follow the same probability distribution; generalizations obtained under more general conditions are mentioned. Illustrations are provided, which demonstrate the usefulness of the laws in providing theoretical justification for: the frequentist concept of probability; the strong consistency of the sample mean and variance estimators; the consistent estimation of the
P
value associated with a Monte Carlo test.
Heteroscedasticity and Complex Variation
Authors: Goldstein Harvey 
Published: September 2014

Abstract
In the standard generalized linear model, the residual variance is assumed to be a constant or a well‐defined function of the linear predictor. In many applications, however, this assumption may not hold and interest will often focus on modeling the residual variation as a function of further explanatory variables, such as gender or age. The article shows how to formulate such models and provides extensions to the multilevel case and to the modeling of a full covariance matrix.
Star and Profile Plots
Authors: Everitt Brian S. 
Published: September 2014

Abstract
Star and profile plots are graphical displays for multivariate data. They consist of an array of small individual plots, each corresponding to a single multivariate observation. In any individual plot, every variable is represented by a line whose length is proportional to the value of the variable. In a star plot, the lines form spokes radiating from a central point, while in a profile plot, they are vertical spikes. In each case, the ends of the lines are connected across variables.
These displays can provide useful insight into the structure of the data, especially when groups of plots with similar shapes, or particular individuals that seem different from the rest, can be identified.
History of Analysis of Variance
Authors: Tweney Ryan D. 
Published: September 2014

Abstract
First used by behavioral scientists in the 1930s, use of Analysis of Variance (ANOVA) grew quickly after World War II, and exactly paralleled incorporation of statistical significance testing in general. Detailed consideration of this history suggests several reasons for the pattern of slow growth, followed by rapid institutionalization. In particular, ANOVA stood duty as a warrant of scientific legitimacy among behavioral scientists, a fact that may also be relevant to understanding recent critiques by psychologists of its overuse and misuse.
History of Psychometrics
Authors: Buchanan Roderick D. , Finch Susan J. 
Published: September 2014

AbstractPsychometrics developed as a means for measuring psychological abilities and attributes, usually via a standardized psychological test. Its emergence as a specialized area of psychology combined entrenched social policy assumptions, existing educational practices, contemporary evolutionary thinking, and novel statistical techniques. Psychometrics came to offer a pragmatic, scientific approach that both fulfilled and encouraged the need to rank, classify, and select people.
History of Intelligence Measurement
Authors: Weidman Nadine 
Published: September 2014

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.
Binomial Test
Authors: Wagner‐Menghin Michaela M. 
Published: September 2014

Abstract
This entry illustrates the binomial distribution and uses a common coin toss experiment as an example for calculating the probabilities of the binomial distribution. The binomial test is then introduced as a valuable test when dealing with dichotomous variables. Using the introduced coin toss experiment, the statistical hypothesis of this test and the calculation of an exact binomial test with and without software are illustrated.
N
eyman–
P
earson Inference
Authors: Nickerson Raymond S. 
Published: September 2014

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.
Classical Statistical Inference: Practice Versus Presentation
Authors: Harris Richard J. 
Published: September 2014

AbstractClassical statistical inference is a useful tool in determining whether sufficient evidence has been marshaled to establish the direction of a population effect. Ifp(the probability that a sample effect as large as or larger than the observed effect size would be obtained when sampling from a population in which the effect size is zero) is very low, we conclude that the population effect goes in the same direction as the sample effect. Otherwise, we conclude that there is insufficient evidence to be confident of the direction of the effect in the population. Misunderstanding and misrepresentation of this logic has a number of unfortunate consequences against which I attempt to inoculate the reader.
Robustness of Standard Tests
Authors: Wilcox Rand R. 
Published: September 2014

AbstractConventional hypothesis‐testing methods such as student'st, the ANOVAFtest, 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.
Multiple Comparison Procedures
Authors: Keselman H. J. , Holland Burt , Cribbie Robert A. 
Published: September 2014

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.
Sensitivity Analysis: Overview
Authors: Wakefield Jon 
Published: September 2014

AbstractIn an observational study, there is always the possibility of bias due to unmeasured variables, measurement error in explanatory variables and/or selection bias; and randomized studies are also not immune to at least some of these biases. A sensitivity analysis provides a means of asking a series of ‘what if?’ questions, in order to determine the robustness of the observed association to sources of bias.
Bradley–Terry Model
Authors: Marden John I. 
Published: September 2014

AbstractIn a paired comparison experiment, several judges compare several objects. Each judge performs a number of paired comparisons, that is, considers a pair of the objects, and indicates which of the two is preferred. The Bradley–Terry model is a simple but powerful model in which each object is equipped with a measure of overall strength, and the chance that one object is preferred to another depends on only their relative strengths. The model is useful not only for preference data but also for competitions, for example, among sports teams.
Measurement: Overview
Authors: Michell Joel 
Published: September 2014

Abstract

Measurement 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 the
traditional 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 a
representational 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 the
operational 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.
Multidimensional Item Response Theory Models
Authors: Ackerman Terry A. 
Published: September 2014

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.
Validity Theory and Applications
Authors: Sireci Stephen G. 
Published: September 2014

Abstract
Theories regarding ways in which test scores should be validated are about as old as the practice of testing itself. In this entry, I describe contemporary validity theory using an historical perspective. I also discuss current approaches for validating tests and other measures used in the behavioral sciences. Validity is a complex topic involving both philosophy of science and statistical methods. Thankfully, there is a long history to guide those who need to evaluate particular measures for specific purposes.
Markov ChainMonteCarlo Item Response Theory Estimation
Authors: Keller Lisa A. 
Published: September 2014

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.
Reliability: Definitions and Estimation
Authors: Mead Alan D. 
Published: September 2014

Abstract

Reliability, ρ

XX
′

, is the degree to which test scores are precise and free from error. Classical test theory defines reliability as the ratio of true score variance to observed score variance: . This article provides a comprehensive definition of reliability and then describes the methods by which reliability is commonly estimated, including their limitations. The article then discusses several closely‐related topics: the standard error of measurement; reliability in subpopulations; the Spearman‐Brown prophesy formula; true score correlations; reliability of speeded tests; and the relationship between classical reliability and item response theory information.
New Item Types and Scoring
Authors: Drasgow Fritz , Mattern Krista 
Published: September 2014

Abstract

The advent of the computer has broadened the concept of testing and assessment. Previously, tests were mainly restricted to a paper‐and‐pencil medium and a multiple‐choice format; however, the computer coupled with recent advancements in its technological sophistication has afforded test developers much more freedom in the types of tests they create. Much research in this area has been devoted to the development of
innovative item types
, which utilize the computer's capabilities to improve assessment or assess characteristics that traditional items cannot. Innovative items allow for a more extensive assessment of individual differences; however, the benefits of these new items do not come without a cost. Scoring innovative items is usually not as simplistic as employing a multiple‐choice key, and, often, the items require extremely complicated scoring algorithms. In this chapter, we provide a review of innovative item types and their accompanying scoring issues. Owing to limited space and a rapidly changing field, it is impossible to provide an exhaustive review; therefore, we focus on the most recent and influential research in the field.
Heuristics: Fast and Frugal
Authors: Hoffrage Ulrich 
Published: September 2014

AbstractFast and frugal heuristics can be used to make cue‐based inferences about an uncertain world. They are models of bounded rationality in the sense that they continue to yield a good performance even under conditions of limited time, limited knowledge, and limited computational complexity. They are specified in terms of search rules, stopping rules, and decision rules. They are robust and can outperform more complex strategies, because and not in spite of their simplicity.
Model Evaluation
Authors: Navarro Daniel J. , Myung Jay I. 
Published: September 2014

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.
Proximity Measures
Authors: Bock Hans‐Hermann 
Published: September 2014

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.
Multivariate Outliers
Authors: Fieller Nick 
Published: September 2014

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.