bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hill's Criteria of Causation
Authors: Goodman Karen J. , Phillips Carl V. 
Published: September 2005

Abstract

The term
criteria of causation
(or
causal criteria
) is often applied to Sir Austin Bradford Hill's (8) list of factors to consider before inferring causation from an observed association. This list is widely taught and cited, despite widespread cautions about the limitations of the list, by Hill and others, and despite lack of evidence that invoking such criteria is useful. This entry describes Hill's views on assessing causation and what others have since said on the subject of causal criteria.
Historical Controls
Authors: Berger Vance W. , Shafran Randi 
Published: September 2005

Abstract
Randomized trials, in which a single group of units is randomly allocated to two or more treatment conditions and then studied in parallel over time, is the gold standard methodology for reducing biases. Yet not every comparison is amenable to a randomized trial. For example, one cannot randomize subjects to their genders or races. Furthermore, even some comparisons that could theoretically be amenable to study with a randomized trial would not be, for ethical reasons. This would include studies of tobacco. Nonrandomized studies may still be parallel over time, in that all treatment groups are treated and studied over the same time interval. In some cases, though, one needs to use historical controls, and perform a before‐and‐after comparison. Strong conditions are required for such comparisons to be reliable.
History of Analysis of Variance
Authors: Tweney Ryan D. 
Published: September 2005

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 Multivariate Analysis of Variance
Authors: Hershberger Scott L. 
Published: September 2005

AbstractMultivariate analysis of variance (MANOVA) can be thought of as a multivariate generalization of analysis of variance (ANOVA), in which more than one dependent variable is used to evaluate group differences. Wilks introduced lambda (Λ) in 1932 as a statistical test of the equality of group centroids. Wilks's Λ was derived from the likelihood ratio of the null and non‐null hypotheses usually specified in MANOVA. Subsequently, other MANOVA test criteria were developed, although all share with Λ the difficulty of unusually complex sampling distributions. Instead of using Λ directly, either an approximate chi‐square orFratio transformation of Λ is preferred.
History of Mathematical Learning Theory
Authors: Lovie Sandy 
Published: September 2005

Abstract
The article outlines the story of modern mathematical learning theory from 1950. In particular, the account provides a brief comparison of the earlier work by Hull with the newer approaches by Estes, Bush and Mosteller, and Luce. It is noted that the latter were thoroughgoing probabilists in that they argued that all learning could only be modeled by stochastic mechanisms.
History of Psychometrics
Authors: Buchanan Roderick D. , Finch Susan J. 
Published: September 2005

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 Surveys of Sexual Behavior
Authors: Everitt Brian S. 
Published: September 2005

AbstractAccording to psychiatrist Anthony Storr, ’sexual intercourse is one of the most natural and certainly the most rewarding and the most life‐enhancing of all human experiences'. Few of us would argue with that, but although sex plays a vital part in our lives, it has for long periods been associated with guilt and shame, rather than enjoyment. Perhaps this explains why it is only in the last hundred years or so that psychologists, biologists, and others have tried to discover the truth about human sexual behavior in a scientific and objective manner. One of the main tools used in such investigations has been the sample survey, and this paper presents a brief (and therefore necessarily incomplete) history of surveys about sex.
History of the Control Group
Authors: Dehue Trudy 
Published: September 2005

AbstractIn contemporary behavioral and social research, scientific experimentation means comparing experimental groups to control groups and establishing the statistical significance of the resulting averages. Yet, this design is of a rather recent date. From the early nineteenth century, scientific experimentation with human beings has been a much debated topic. The rule of comparing groups, however, did not appear in methodological manuals until the second decade of the twentieth century. This article presents a history of the control group. It first discusses nineteenth‐century methodological views on human science experimentation, and explains why the notion of the control group could not be conceived at the time. Next, it describes and explains the introduction of control groups in the course of the twentieth century. By analyzing the nineteenth‐century values that excluded group comparison, it aims to identify the twentieth‐century principles endorsing it.
Inbred Strain Study
Authors: Wade Claire 
Published: September 2005

Abstract
Genetic contributions in complex trait analyses can be controlled or removed by the use of inbred strains as the test subjects. To be regarded as inbred strains, the mammals concerned must have been subjected to at least 20 generations of full‐sib mating (the expected inbreeding coefficient will be 98.6% or greater). Variation in the test scores observed within inbred strains will be of either environmental (treatment) origin or otherwise caused by random experimental error, since genetic variation is largely absent.
Incidence
Authors: Grietens Hans 
Published: September 2005

AbstractIncidence is determined as the number of new cases of a disease in a population at a specified interval of time. Incidence rates provide information about the number of new cases or cases added to the sick population. Incidence studies are part of epidemiological research. They are conducted in medicine, psychiatry, and behavioral science. By means of an incidence study, one can truly identify individuals at risk for a disease, or risk factors. There is a dynamic relationship between prevalence and incidence, since prevalence rates are a function of both the incidence and the duration of a disease.
Independent Pathway Model
Authors: Rijsdijk Frühling 
Published: September 2005

Abstract

While traditional multivariate statistical methods, can describe correlational patterns of a set of variables (e.g., psychiatric symptoms or problem behaviors in children), they cannot provide insight into why certain symptoms or behaviors tend to co‐occur in a population. This can be achieved using methods of
multivariate genetic analysis
. In multivariate genetic models 2 main classes can be distinguished, which differ in the way the common factors are assumed to influence the different observed variables. The way unique factors influence the observed variables is the same in both classes. The first class of models is referred to as
common pathway models
or psychometric models. Common genetic and environmental factors influence all observed variables via a single psychometric factor, or underlying latent liability. The second class of models is referred to as independent pathway or biometric models. The common genetic and environmental factors influence the observed variables directly, without an intermediate higher order factor. As a result, the common genetic and environmental do not necessarily cause similar groupings. This means that different clusters of variables can be identified for shared genetic and environmental effects. For this reason biometric models can yield different results compared to higher order phenotypic factor models, which implicitly assume identical groupings for genetic and environmental effects.
Information Theory
Authors: Mair Patrick , von Eye Alexander 
Published: September 2005

Abstract

Communication and information theory were founded by Shannon (1948). Shannon and Weaver (1949) developed the classical system of communication and the mathematical bases of communication. Information theory is concerned with measuring the degree to which a message provides one with knowledge one did not possess before. The broadest understanding of information theory includes all problems for which it is sensible to use the word
information
in its usual sense. The amount of information that is transmitted is scaled in units of
entropy
. Information theory allows one to analyze and assess a situation in which a signal that was selected from an
a priori
specified set is transmitted through a channel with limited capacity. The output of the channel is not determined by the input. Modern applications of information theory can be found in electronics and in the social sciences.
Intention‐to‐Treat
Authors: Molenberghs Geert 
Published: September 2005

Abstract
The randomized clinical trial paradigm is sketched, as well as the foundations on which its validity is based. Issues that jeopardize this validity, such as patient noncompliance and early withdrawal, are discussed. Intention to treat, being an important historical answer to this problem, is introduced, together with a set of criticisms and an indication of alternative approaches.
Interaction Effects
Authors: Aiken Leona S. , West Stephen G. 
Published: September 2005

Abstract
Interaction effects refer to the effects of particular unique combinations of variables on an outcome that would not be expected from their average effects. When two variables interact, the conditional effect of one variable at a particular value of the other variable depends on the specific value of the other variable. One variable moderates the effect of the other variable. When variables interact, the effects of the two variables in combination are nonadditive; their simultaneous effect is not the sum of their individual effects. Continuous and categorical variables may interact; a variety of patterns of interactions are possible.
Intraclass Correlation
Authors: Field Andy P. 
Published: September 2005

Abstract
Intraclass correlations are used to assess the consistency between measures of the same class (i.e., measures of the same thing). Two common uses are in comparing paired data (such as twins) on the same measure and assessing the consistency between judges' ratings of a set of objects. There are different conceptualizations of the intraclass correlation and the variance components used to calculate them in these different models. There is an important distinction between the cases when the measures are a single score or an average of multiple scores, and when a measure of consistency or a measure of absolute agreement is required.
Intrinsic Linearity
Authors: Goldsmith L. Jane , Berger Vance W. 
Published: September 2005

Abstract
In some cases in which a response variable is related to one or more predictor variables through a nonlinear relationship, suitable transformations of the response variable, predictor variables, parameters, or various combinations can lead to a linear model, thereby enabling the use of the powerful and efficient theory of linear models for estimation and prediction. Care must be taken with the use of such transformations to ensure that the resulting linear model has an error term of the appropriate, additive form.
Item Response Theory (
IRT
) Models for Polytomous Response Data
Authors: Keller Lisa A. 
Published: September 2005

Abstract
Many measurement tools are constructed of items that can be scored in several categories. For example, essay items, or other constructed response items, or personality and attitude inventories composed of Likert‐type items. Several item response models have been developed to deal with items of this type. The models are extensions of the dichotomous items presented in this volume. The five models discussed are the Graded Response Model, the Rating Scale Model, the Partial Credit Model, the Generalized Partial Credit Model, and the Nominal Response Model. Descriptions of the models are provided, along with graphical representations, and descriptions of how to interpret the parameters. The entry concludes with a list of articles that have applied these models in different contexts.
Item Response Theory (IRT) Models for Rating Scale Data
Authors: Engelhard George 
Published: September 2005

AbstractThe purpose of this entry is to provide a brief description of several IRT models that can be used to analyze rating scale data. Rating scale data are defined as responses scored in two or more ordered categories. Rating scale data represent the most common formats for collecting information from examinees or raters. The basic idea that motivates the use of IRT models for rating scale data is that the scoring ofm + 1 ordered categories with ordered integers (0, 1, …,m) using the assumption that there are equal intervals between the categories may not be justified. IRT models provide a framework to explicitly examine this assumption, and parameterize the categories without this assumption. The specific IRT models that are described in this entry are direct models (Partial Credit Model, Generalized Partial Credit, and Rating Scale Model) and indirect models (Graded Response Model and Modified Graded Response Model). Graphical procedures are used to illustrate various aspects of these IRT models.
Maximum Likelihood Item Response Theory Estimation
Authors: Rupp André A. 
Published: September 2005

AbstractAn overview and short didactic for three common estimation techniques for parameters in item response theory models are described: (a) joint maximum likelihood, (b) conditional maximum likelihood, and (c) marginal maximum likelihood. Specifically, likelihood equations fundamental to all three estimation approaches are presented first and joint as well as conditional maximum likelihood estimation are briefly described next. The main part of the entry focuses on an extended description of marginal maximum likelihood estimation and illustrates, in a step‐by‐step fashion, how it makes use of the EM algorithm and a fully Bayesian estimation framework to estimate item parameters. A brief description of how person parameters are subsequently estimated follows. Finally, a few words on alternative estimation approaches are offered.
Kernel Smoothing
Authors: Hazelton Martin L. 
Published: September 2005

AbstractKernel smoothing is a nonparametric technique for exposing structure or trend in data. It can be applied in a wide variety of settings, including the problems of density estimation and nonparametric regression on which we focus attention. In each case, the performance of kernel methods is highly dependent on the choice of bandwidth, a parameter that controls the amount of smoothing applied to the data.
K
olmogorov–
S
mirnov Tests
Authors: Berger Vance W. , Zhou YanYan 
Published: September 2005

Abstract
While often confused, the Kolmogorov–Smirnov test and the Smirnov test are actually distinct. Specifically, the Kolmogorov–Smirnov test is used to test the goodness of fit of a given set of data to a theoretical distribution, making this a one‐sample test. In contrast, the Smirnov test is a two‐sample test, used to determine if two samples appear to follow the same distribution. The intuition behind the two tests is the same, however, in that both compare cumulative distribution functions, either two empirical cumulative distribution functions for the two‐sample Smirnov test, or one empirical cumulative distribution function and one known cumulative distribution function for the one‐sample Kolmogorov–Smirnov test.
Kruskal–Wallis Test
Authors: Sawilowsky Shlomo , Fahoome Gail 
Published: September 2005

AbstractThe nonparametric Kruskal‐Wallis test is an extension of the Wilcoxon‐Mann‐Whitney test. The null hypothesis is that thekpopulations sampled have the same average (median). The alternative hypothesis is that at least one sample is from a distribution with a different average (median). This test is an alternative to the parametric one‐way analysis of variance F test.
K
urtosis
Authors: Wuensch Karl L. 
Published: September 2005

Abstract
Kurtosis is defined with respect to the fourth moment about the mean. It is related to the shape of a distribution, but not in a simple fashion. Platykurtic distributions have less kurtosis than does a normal distribution, are heavy in their shoulders, tend to be light in their tails, and may be flatter on top than a normal distribution, or may be U‐shaped. Leptokurtic distributions have more kurtosis than does a normal distribution, are light in their shoulders, tend to be heavy in their tails, and may have a more peaked top than does a normal distribution. High kurtosis can signal the presence of outliers in one or both tails.
Laws of Large Numbers
Authors: Kay Jim W. 
Published: September 2005

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.
Logistic Regression
Authors: Christensen Ronald 
Published: September 2005

AbstractLogistic regression is a method for predicting the outcomes of ‘either‐or’ trials. Either‐or trials occur frequently in research. A person responds appropriately to a drug or does not; the dose of the drug may affect the outcome. A person may support a political party or not; the response may be related to their income. A person may have a heart attack in a 10‐year period; the response may be related to age, weight, blood pressure, or cholesterol. We discuss basic ideas of modeling in the section titled ‘Basic Ideas’ and basic ideas of data analysis in the section titled ‘Fundamental Data Analysis’. Subsequent sections examine model testing, variable selection, outliers and influential observations, methods for testing lack of fit, exact conditional inference, random effects, and Bayesian analysis.
Log‐Linear Models
Authors: Vermunt Jeroen K. 
Published: September 2005

AbstractLog‐linear analysis is a widely used method for the analysis of multivariate frequency tables obtained by cross‐classifying sets of nominal, ordinal, or discrete interval level variables. Attention is paid to the standard hierarchical log‐linear modelling framework and to more advanced types of log‐linear models that make it possible to impose interesting restrictions on the model parameters, as well as to ‘regression analytic’, ‘path‐analytic’, and ‘factor‐analytic’ variants of log‐linear analysis.
Log‐linear
R
asch Models for Stability and Change
Authors: Meiser Thorsten 
Published: September 2005

Abstract
This entry presents Rasch models for the analysis of longitudinal data and log‐linear representations of Rasch models to test specific hypotheses about change. The use of log‐linear Rasch models is illustrated by an empirical example concerning the development of arithmetic competence in elementary school. The final section discusses model extensions for the analysis of polytomous items, multidimensional latent constructs, and several latent subpopulations in longitudinal research.
M
Estimators of Location
Authors: Wilcox Rand R. 
Published: September 2005

Abstract

An approach to developing measures of location is to search for some value, say
c
, that is close to the values in a sample, say
X
1
, …,
X

n

. If we measure closeness with the sum of squared differences between each of the
n
observations and
c
, this leads to taking
c
to be the sample mean. But the sample mean can perform poorly in terms of achieving a small standard error when sampling from nonnormal distributions. M‐estimators attempt to correct this problem by replacing squared error with some other measure of closeness. Certain M‐estimators have standard errors that compete well with the sample mean under normality, but they continue to perform well in situations in which the sample mean performs poorly.
Mahalanobis Distance
Authors: Huberty Carl J. 
Published: September 2005

Abstract

The Mahalanobis distance is a generalization of the distance concept to
p
‐dimensional correlated data. The measure uses an appropriate covariance matrix to take account of differences in variable variances and correlations between variables. It is frequently employed in multivariate statistical methods, for example, in multivariate analysis of variance, discriminant analysis, and cluster analysis.
Mail Surveys
Authors: Mangione Thomas W. 
Published: September 2005

AbstractMail surveys are much less costly to conduct than telephone surveys or in‐person surveys and therefore are an attractive method of data collection for researchers. However, in general, mailed surveys have suffered from low response rates which limits their applicability. Extensive research conducted over the past few decades has documented a variety of procedural elements, which if followed, can overcome low response rates. In addition to postage‐paid return envelopes, strong respondent letters, and clear promises of respondent confidentiality or anonymity, the two most important procedures to incorporate in the study design are using as many as three reminders and including financial incentives for participation. Additional elements such as personalization, prenotification, modest length, and utilization of soft deadlines also have been shown to increase response rates slightly. By following these procedures, data collected through mail surveys can be as valid as data collected through telephone or in‐person methods.
Mantel–Haenszel Methods
Authors: Fidalgo Ángel M. 
Published: September 2005

AbstractMantel–Haenszel (MH) methods constitute a highly flexible methodology for assessing the degree of association between two categorical variables, whether they are nominal or ordinal, controlling the effect of other variables. These procedures, based on minimal assumptions, provide both statistical tests of the null hypothesis of no‐association and estimators of the degree of association. The present entry considers the MH statistics most commonly used for the particular case ofQs contingency tables with dimensions 2 × 2 and the general case ofQtables with dimensionsR × C.
Marginal Independence
Authors: Berger Vance W. , Zhang Jialu 
Published: September 2005

Abstract

Two variables, say
X
and
Y
, are marginally independent if their joint distribution is the product of the two marginal distributions. In the discrete case, inspecting only the marginal table of
X
‐by‐
Y
may lead to false conclusions if the association between
X
and
Y
is not the same across different levels of another covariate, say
Z
. To prevent such misleading conclusions, partial tables of
X
and
Y
at each level of the confounding covariate
Z
should be displayed.