bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian Belief Networks
Authors: Phillips Lawrence D. 
Published: September 2005

AbstractBayesian Belief Networks (BBNs) are directed graphs of conditional probabilities that capture the knowledge of experts along with hard data. BBNs aid rational reasoning in situations of uncertainty, and permit inferences to be made about hypotheses of interest. On the basis of the laws of probability, including Bayes's theorem, they provide a clear, visual representation of situations of very considerable complexity, in which the uncertainty about target events is extended to include many relevant events. BBNs can handle uncertain inferences from data to intervening variables to hypotheses, and they can take account of the unreliability of particular data sources.
Bernoulli Family
Authors: Senn Stephen 
Published: September 2005

Abstract
In his entertaining but irreverent book on the history of mathematics, Eric Temple Bell took the case of the Bernoullis as being clear proof of the heritability of mathematical genius. This remarkable Swiss dynasty produced at least ten mathematicians of note. Because the names, James, John, Nicholas, and Daniel, appear in different generations and also, depending on the account, in Latin, German, French or English forms, identification of individuals is perilous.
Binomial Distribution: Estimating and Testing Parameters
Authors: Berger Vance W. , Zhou YanYan 
Published: September 2005

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.
Classical Statistical Inference: Practice Versus Presentation
Authors: Harris Richard J. 
Published: September 2005

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.
Item Bias Detection: Classical Approaches
Authors: Mellenbergh Gideon J. 
Published: September 2005

AbstractUnder the assumptions of Classical Test Theory (CTT), a person's observed test score is an estimate of his (her) true test score. Therefore, CTT‐based methods to detect differentially functioning items use the test score to equate members of different groups. An item is considered to be differentially functioning between two groups when item responses differ between members of the two groups at the same test score. Five methods for the detection of differentially functioning dichotomous item responses and their generalization to ordinal‐polytomous item responses are discussed.
Classical Test Models
Authors: Muñiz José 
Published: September 2005

Abstract
The term ‘Classical Test Theory’ covers an entire set of psychometric models and procedures whose main objective is to solve the psychometric problems arising in the measurement of variables in the social sciences. Classical test theory permits us to estimate the psychometric properties of the tests, questionnaires, and scales used in social science measurement, guaranteeing that measurements are made with the appropriate scientific and technical rigor. The first work in this area appeared at the beginning of the twentieth century, and by the 1960s, the core of the classical approach was well established. The classical model assumes that a person's empirical score in a test is made up of two additive components, the true score corresponding to the assessed person in that test, and a certain measurement error. From this simple model, and with the addition of some assumptions and the definition of parallel tests, we obtain the principal classical formulas that allow the estimation of the psychometric properties of tests, such as reliability and validity.
Collinearity
Authors: Chen Gilad 
Published: September 2005

Abstract

Collinearity reflects situations in which two or more independent variables are perfectly or nearly perfectly correlated. In the context of multiple regression, collinearity violates an important statistical assumption and results in uninterpretable and biased parameter estimates and inflated standard errors. Regression diagnostics such as
variance inflation factor
(VIF) and
tolerance
can help detect collinearly, and several remedies exist for dealing with collinearity‐related problems.
Comorbidity
Authors: Rhee S. H. , Hewitt John K. , Corley R. P. , Stallings M. C. 
Published: September 2005

AbstractComorbidity between psychiatric disorders often exceeds the rate expected by chance alone. Increased knowledge regarding the causes of comorbidity between two psychiatric disorders may have a significant impact on research regarding the classification, treatment, and etiology of both of disorders. We examined the validity of common methods used to test alternative comorbidity models.
Correlation
Authors: Kornbrot Diana 
Published: September 2005

Abstract
Correlation describes whether two variables ‘go together’. Correlation is a general term, covering various different kinds of association or relationship between two variables. Correlation does not imply causality. Correlations may be positive or negative, linear (looking like a straight line) or nonlinear (curved), and strong or weak. There are several different measures of correlation. Correlation measures range from +1, indicating perfect positive agreement to −1, indicating perfect negative agreement. The most appropriate measure depends on the type of measurement scale and distribution of both variables and the shape of the relationship between them.
Number of Matches and Magnitude of Correlation
Authors: Rovine Michael J. 
Published: September 2005

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).
Cultural Transmission
Authors: Hershberger Scott L. 
Published: September 2005

Abstract
Cultural transmission refers to the transfer of the knowledge, values, and ideas within a population. It is also a nongenetic form of natural selection in which behavioral adaptations that arise from cultural influences are passed from one generation to the next. Importantly, cultural transmission occurs between an individual and the individual's conspecifics, both between generations and within generations. ‘Cultural transmission’ is not synonymous with ‘environmental influence’ – only ‘individuals’ are the relevant part of the environment critical to cultural transmission. The transfer of information is based on social learning specifically, and not on other types of learning such as behavioral reinforcement. Mathematical models of cultural transmission have been developed that emphasize either the similarities or the differences among individuals within a population. Cultural transmission may also be an important cause of correlated genotypes and environments.
De
F
ries–
F
ulker Analysis
Authors: Rende Richard , Slomkowski Cheryl 
Published: September 2005

Abstract
DeFries‐Fulker (DF) analysis involves a series of models that provide traditional estimates of genetic and environmental parameters for individual differences on a trait, along with unique parameters specific to the etiology of extreme levels of a trait. These models are presented along with reference to a new model‐fitting implementation of the DF approach.
Differential Item Functioning
Authors: Rogers H. Jane 
Published: September 2005

Abstract
Differential item functioning (DIF) occurs when individuals at the same level on the trait(s) or construct(s) being assessed but from different subpopulations have unequal probabilities of attaining a given score on the item. Methods for investigating DIF have been developed for both dichotomously and polytomously scored items. These methods may be classified by whether they condition on an unobserved or observed variable. Item response theory, logistic regression, Mantel–Haenszel, and SIBTEST procedures for dichotomously scored responses and their extensions to polytomous responses are currently the most widely used methods for detecting DIF.
Causal‐Comparative Study
Authors: Busk Patricia L. 
Published: September 2005

Abstract

A nonexperimental design that is employed to explore cause‐and‐effect relationships is called
ex post facto
or causal‐comparative. The title
ex post facto
is applied to studies in which data are gathered retrospectively, whereas causal‐comparative is used when data are gathered from groups and the independent variable is not manipulated as in an experiment.
Ecological Fallacy
Authors: Kreft Ita G. G. 
Published: September 2005

Abstract
Before the invention of multilevel data analysis, researchers have struggled with the question how to analyze data that consist of observations in natural occurring groups. The choice is to analyze individuals irrespective of group membership, or analyze groups, and ignore individual differences within groups. A mistake, called ecological fallacy, may appear when inferences are made for individuals based on group data analysis and vice versa. The following examples show some dramatic fallacies and explain the reason why cross‐level inferences can lead to a wrong interpretation of the data.
Eta and Eta Squared
Authors: Field Andy P. 
Published: September 2005

Abstract

Eta squared (η
2
) is an elegant and simple way to express an effect size (typically the effect of manipulations across several groups). Put simply, it is the ratio of variability explained by the manipulations (the group means) to the total variability. It is an accurate measure for the sample, but is a biased estimate of the effect in the population.
Event History Analysis
Authors: Vermunt Jeroen K. , Moors Guy 
Published: September 2005

AbstractThe purpose of event history analysis is to explain why certain individuals are at a higher risk of experiencing the event(s) of interest than others. This can be accomplished by using special types of methods which are usually referred to as hazard models. Two special features of hazard models are that they can deal with censored observations and with covariates that change their values during the observation period.
Factor Analysis: Multiple Groups
Authors: Little Todd D. , Slegers David W. 
Published: September 2005

Abstract
The general factor model, when extended to include mean‐level information, is a very powerful tool for cross‐group and longitudinal comparisons. The multiple‐group mean and covariance structures (MACS) approach is particularly useful for making cross‐group (or cross‐time) comparisons because it allows for (a) simultaneous estimation of all parameters in each group (or at each occasion) and (b) direct statistical comparisons of the estimated parameters across the groups (or occasions). Because the means of measured indicators are included explicitly in the analyses, they provide a basis to make a very strong test of the validity of construct comparisons (i.e., measurement equivalence) across groups and across time. Specifically, strong factorial invariance indicates that constructs are fundamentally similar in each group or across time (i.e., comparable) and hypotheses about the nature of possible group‐ or time‐related influences can be meaningfully tested on any of the constructs' basic moments (means, variances, or covariances) across time or across each group whether the groups are defined on the basis of culture, gender, or any other grouping criteria.
Generalized Linear Mixed Models
Authors: Hedeker Donald 
Published: September 2005

AbstractGeneralized linear models (GLMs); represent a class of regression models for several types of dependent variables where the linear predictor includes only fixed effects. Incorporation of random effects into GLMs yields the class of models known as generalized linear mixed models (GLMMs). Random effects are typically included for analysis of clustered and/or longitudinal data to account for the correlation of the data. GLMMs are especially useful for analysis of correlated nonnormal data, and the term GLMMs often refers to models for these kinds of data.
History of Correlational Measurement
Authors: Cowles Michael 
Published: September 2005

Abstract

The beginnings of attempts to index the relationship between two or more variables began in the nineteenth century. A number of mathematicians produced theoretical underpinnings that lead to the concept of correlation, but none appreciated the practical applications of the method. Galton looked at correlation in the context of heredity with the aim of providing objective mathematical descriptions that would bolster his belief in, and demonstrate the truth of the social philosophy of planned heredity that he named
eugenics
. Pearson, a mathematician and also a eugenist, arrived at the best formula for the coefficient of correlation and it was named after him. Later, the methods of partial and multiple correlation followed. The methods of factor analysis were introduced and techniques for handling relationships among ordinal and nominal data added to the growing collection of statistical methods in the behavioral and biological sciences.
Industrial/Organizational Psychology
Authors: Landis Ronald S. , Kaplan Seth A. 
Published: September 2005

Abstract

Industrial and organizational (IO) psychologists utilize a variety of statistical techniques in their research. A nine‐year review of two leading journals (
Journal of Applied Psychology
and
Personnel Psychology
) in the field reveals that correlational techniques appear in the largest percentage of studies. More complex methods such as structural equation modeling (SEM) and hierarchical linear modeling (HLM) appear in proportionally fewer studies. Representative examples of how various statistical techniques are used in IO research are provided, and possible reasons for the observed results are presented. In addition, IO psychologists' contributions to statistics and the role of statistics in IO theory development are discussed.
J
onckheere–Terpstra Test
Authors: Lunneborg Clifford E. 
Published: September 2005

Abstract
For the one‐way design, the Jonckheere‐Terpstra procedure tests the null hypothesis of no difference in response magnitude across treatments against an ordered alternative, that response magnitude increases over a pre‐specified ordering of the
treatments. Where the treatment levels can be ordered according to expected response magnitude (e.g., drug dosages, stimulus intensities, and hours of practice), the Jonckheere–Terpstra test (1954) employs a more coherent, directional alternative hypothesis, that is, the response magnitude does not decrease for any treatment level increase and actually increases for one or more treatment level increases.
K
endall's
T
au‐τ
Authors: Kornbrot Diana 
Published: September 2005

Abstract

Kendall's tau, τ, is a correlation measure of association based on ranks of scores rather than raw scores. It is based on the number of swaps needed to put the second variable into exactly the same or exactly the opposite order to the first variable. 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. Methods for calculating τ and procedures for hypothesis testing are provided. Kendall's τ is compared with Spearman's ρ and Pearson's
r
.
Data Mining
Authors: Hand David J. 
Published: September 2005

Abstract
Data mining is the discovery of structures and patterns in large data sets. There are two aspects to data mining: model building and pattern detection. Model building in data mining is very similar to statistical modeling, although new problems do arise because of the large sizes of the data sets and the fact that data mining is secondary data analysis. Pattern detection seeks anomalies or small local structures in data, with the vast mass of the data being irrelevant. Although some subdisciplines of statistics have examined special cases of this problem, the bulk of the work on pattern detection to date has been computational, with an emphasis on algorithms.
Latent Transition Models
Authors: van de Pol Frank , Langeheine Rolf 
Published: September 2005

Abstract
Flows between states for discrete periods of time can be described with a Markov chain, a process without memory. More realistic for social science data is a heterogeneous mixture of Markov chains, of movers and stayers. Alternatively, or in conjunction with heterogeneity in the tendency to change, one can take measurement unreliability into account, using latent or indirectly measured Markov models. The basic model variants are explained with data examples. References to overview papers are given.
Multivariate Analysis of Variance
Authors: Hartung Joachim , Knapp Guido 
Published: September 2005

Abstract
Multivariate analysis of variance (MANOVA) techniques are used to analyze simultaneously the influence of qualitative factors on more than one quantitative variable. MANOVA allows testing the null hypothesis that the mean responses of the quantitative variables are the same across all the levels of a factor averaged over all the other factors against the alternative hypotheses that there are at least two factor levels that have different mean responses. We describe the four test statistics commonly used for hypothesis testing in MANOVA and frequently implemented in statistical software packages.
Marginal Models for Clustered Data
Authors: Fitzmaurice Garrett M. 
Published: September 2005

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. Longitudinal and repeated measures studies also give rise to clustered data. Marginal models provide a broad class of regression models that are suitable for analyzing diverse types of clustered data (e.g., continuous, binary, and count data). The distinctive feature of marginal models is that they focus on each response within the cluster separately, allowing dependence of the mean response on covariates but not on any random effects or other responses within the same cluster. By focusing on the population mean response, averaged over all clusters, the regression coefficients relating the mean response to covariates are said to have
population‐averaged
interpretations.
M
axwell,
A
lbert
E
rnest
Authors: Bartholomew David J. 
Published: September 2005

Abstract

A.E. Maxwell (1916–1996) spent most of his career at the Institute of Psychiatry, University of London, latterly as head of the Biometrics Unit. He is best known for his co‐authorship (with D.N. Lawley) of the book
Factor Analysis as a Statistical Method
. This established factor analysis as a branch of statistical methodology. His other publications include several books on psychological statistics and he also engaged in extensive consulting and advisory work.
Markov ChainMonteCarlo andBayesian Statistics
Authors: Congdon Peter 
Published: September 2005

AbstractBayesian estimation via repeated sampling algorithms has opened new possibilities in analyzing complex statistical models and data sets. Such algorithms are now the standard for estimating parameters, making other inferences (e.g., posterior probabilities on hypotheses relating to parameters), and in model choice. A set of methods collectively known as Monte Carlo Markov Chain algorithms is discussed as well as issues in their practical implementation.
Measures of Association
Authors: Hershberger Scott L. , Fisher Dennis G. 
Published: September 2005

AbstractMeasures of association are numerical estimates of the magnitude of the statistical dependence between two variables. A multitude of such measures have been proposed; we review some of the most commonly used ones, dividing them into two types. The first is suitable for nominal scale data, and includes measures based on the odds ratio, measures based on Pearson's χ2, and measures of predictive association. The second type is suitable for ordinal scale data, and includes measures of concordance‐discordance, and measures based on derived scores.
Model Selection
Authors: Cortina Lilia M. 
Published: September 2005

Abstract
Given the complexity inherent in behavioral phenomena, there are typically multiple plausible models that can account for any set of observations. The task, then, is to select the model that shows superior qualities in terms of fit, parsimony, interpretability, and consistency with theory. Even then, the researcher cannot claim to have identified the one ‘true’ model, because selection of an excellent model could artifactually result from failure to consider every possible alternative.
Multigraph Modeling
Authors: Khamis Harry J. 
Published: September 2005

AbstractMultigraph modeling makes use of mathematical graph theory to analyze and interpret hierarchical loglinear models. For a given such model, a multigraph is constructed, and from the multigraph, decomposability is determined from a simple combinatorial identity; factorization of the joint distribution is based on a maximum spanning tree; and the conditional independence interpretation comes from the edge cutsets.
Multiple Linear Regression
Authors: West Stephen G. , Aiken Leona S. 
Published: September 2005

Abstract
Multiple regression represents the relationship between a dependent variable and a weighted linear combination of a set of independent variables. Multiple regression can be used to model a wide variety of forms of relationships and types of independent variables. Diagnostic procedures can be used to identify violations of assumptions and outlying data points which adversely affect its performance. Multiple regression can be used to describe relationships, predict future scores on the dependent variable, and test specific hypotheses.
Nonequivalent Group Design
Authors: Reichardt Charles S. 
Published: September 2005

Abstract
A nonequivalent group design is a quasi‐experiment used to assess the relative effects of treatments that have been assigned to groups of participants nonrandomly. Because the participants have been assigned to treatments nonrandomly, differences in the composition of the treatment groups can bias the estimates of the treatment effects. A variety of statistical methods are available for taking account of this selection bias. Each method imposes different assumptions about the nature of the selection effects, but it can be difficult to determine which set of assumptions is most appropriate in a given research setting. Researchers are advised to minimize selection differences by using groups of participants that are initially as similar as possible.