bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conditional Standard Errors of Measurement
Authors: Tong Ye , Kolen Michael J. 
Published: September 2005

Abstract
Errors of measurement for test scores generally are viewed as random and unpredictable. Conditional standard errors of measurement (SEMs) index the amount of error in the measurement process and are required to be reported for tests. Methods of estimating conditional SEMs, based on both classical test theory and generalizability theory, are described in this entry. These methods are based on differing assumptions, but the conditional SEMs estimates obtained from these methods are commonly highly related for educational achievement tests.
Conditional SEMs vary by score level. Conditional SEMs typically have high values for extreme scores and have low values for middle scores on the raw score scale. However, a nonlinear transformation of the raw scores to scale scores can lead to conditional SEMs that have other patterns across score levels.
Misclassification Rates
Authors: Krzanowski Wojtek J. 
Published: September 2005

Abstract
This article surveys the ways in which the error rate incurred by a classifier, that is, the proportion of future incorrect classifications, can be estimated. Theoretically derived rates depend heavily on distributional assumptions; so, in practice, data‐based estimates are generally preferred. Methods based on resubstitution, test‐set estimation, cross‐validation, jackknifing, and bootstrapping are described. The phenomenon of tuning the classifier by using one of these methods to optimize its performance is also explained, and the dangers of overoptimism in assessing error rates of a tuned classifier are highlighted. Interval estimation of error rates is briefly discussed, and an illustrative example is provided.
Estimation
Authors: van de Geer Sara A. 
Published: September 2005

Abstract

This paper is concerned with the relationship between sample data and the probability distribution (
P
), which is supposed to have generated it. The estimation of
P
using an empirically derived distribution is discussed along with specific estimators such as the sample mean, the sample median, and a histogram. Estimation by the method of moments and minimum chi‐square estimation are explained, as are the concepts of bias, mean square error, and sufficiency.
Least Squares Estimation
Authors: van de Geer Sara A. 
Published: September 2005

Abstract

The method of least squares is about estimating parameters by minimizing the squared discrepancies between observed data on the one hand, and their expected values on the other. It is commonly used in the context of a regression model, where one observes pairs of variables (
X
,
Y
), with
X
a covariable and
Y
a response variable. The expectation of the response variable
Y
given the covariable
X
is modeled as a function
f
β
(
X
) depending on parameters β.

We present an explicit expression for the least squares estimator

of β in the linear regression model, and also give its covariance matrix. The calculations are illustrated with a numerical example. The F statistic for testing linear hypotheses on β is also given. We conclude with some extensions.
Maximum Likelihood Estimation
Authors: Enders Craig K. 
Published: September 2005

Abstract
Maximum likelihood estimation identifies the population parameter values most likely to have produced a particular sample of data. Estimation involves the computation of a log likelihood, a value that quantifies the probability that the sample originated from a normally distributed population with a given set of parameters. Standard errors are obtained as a by‐product of estimation, and can be used to conduct significance tests. Differences between full and restricted maximum likelihood are discussed.
Exact Methods for Categorical Data
Authors: Hershberger Scott L. 
Published: September 2005

Abstract
Exact tests evaluate the significance of a test statistic by using the test statistic's empirically derived sampling distribution instead of a theoretical sampling distribution. Preference for exact tests may occur in situations in which the sample size is small, or when the data have distributional deficiencies. Two well‐known exact tests, Pitman's test and Fisher's test, are described.
Expectancy Effect by Experimenters
Authors: Rosenthal Robert 
Published: September 2005

Abstract
Experimenter expectancy effect is the term referring to the unintended effect of experimenters' expectations or hypotheses on the results obtained from their research participants. Experimentally induced expectations have been shown to influence the behavior of psychological experimenters so as to increase the likelihood that their research participants will respond so as to confirm the experimenters' hypotheses or expectations. Experimental evidence for the operation of this phenomenon is described, and strategies for the control of experimenter expectancy effects are outlined.
Factor Analysis: Exploratory
Authors: Pruzek Robert 
Published: September 2005

Abstract
Exploratory factor analysis (EFA) provides a way to account for intercorrelations or covariances among all pairs of variables in a battery using a relatively small number of latent variables called common factors. The method can be especially helpful for analyzing structural relationships in situations where observed variables are approximately linearly related to one another, and when individual‐level observations are fallible, that is, subject to errors of measurement. Exploratory factor analysis has gained prominence in many fields over many years, and it has been the subject of much praise as well as criticism. Psychometric and statistical practice has not fully caught up with contemporary appreciations of factor analytic and related methods, but past and recent developments suggest notable promise for continued applications in applied sciences.
Face‐to‐Face Surveys
Authors: Doyle James K. 
Published: September 2005

Abstract
In a face‐to‐face survey, an interviewer is physically present to ask the survey questions and to assist the respondent in answering them. This survey delivery mode can offer many advantages over mail and telephone surveys in terms of the complexity and quality of the data collected. However, because these advantages come with significantly increased logistical costs as well as additional potential sources of response bias, face‐to‐face surveys are typically conducted only when there are compelling reasons to opt out of other survey delivery modes.
Facet Theory
Authors: Borg Ingwer 
Published: September 2005

Abstract
Facet theory (FT) is a methodology for designing and analyzing observations. A facet classifies objects of interest in one particular way. Several facets can be linked in a mapping sentence to yield schemes for the construction of multifaceted observations. Such designs also serve to predict certain structures for its data. To check these structures, FT has developed particular data analytic methods such as multidimensional scalogram analysis (MSA) or partial order structuple analysis with coordinates (POSAC), or suggests to use main‐stream techniques such as multidimensional scaling (MDS) in particular ways.
Factor Analysis of Personality Measures
Authors: Chaplin William F. 
Published: September 2005

AbstractFactor analysis refers to a broad array of statistical models and techniques that have the general goal of describing underlying patterns in relational data. A common application of factor analysis is to items that assess personality, with the goal of identifying the major dimensions of human personality. These dimensions may be viewed as descriptive or causal depending upon the views of the investigator, and different numbers and types of dimensions have been proposed. Subjective decisions about the items to include in the analysis, the method of factor identification (principle components or principle factors), the number of factors, the rotation of the factors, and the interpretation of the factors all contribute to the diversity of the results obtained with different factor analyses. The goal of factor analysis is generally to simplify the complex structure of human personality, not to fully model that complexity. Thus, recent results from the application of confirmatory factor analysis to personality data that indicate that established factor models do not adequately model all the covariation among personality measures are neither surprising nor particularly relevant.
Factor Analysis: Multitrait–Multimethod
Authors: Wothke Werner 
Published: September 2005

Abstract
Confirmatory factor analysis is the preferred multivariate model for describing multitrait–multimethod matrices. Trait‐only factor analysis assumes that all common variation is due to shared traits. An expanded trait‐method factor model allows for additional variation due to shared methods. Experience shows that the trait‐only model fits few empirical matrices but that the more general trait‐method model is usually plagued by estimation problems. Reasons for the model failure are referenced.
History of Factor Analysis: A Psychological Perspective
Authors: Thorndike Robert M. 
Published: September 2005

Abstract
The history of unrestricted (exploratory) factor analysis is briefly reviewed from the perspective of its contribution to psychological theory, particularly models of mental abilities. Attention is focused on the contributions of Spearman and Thurstone. Various means of judging the quality of a factor solution are described. The logic of factor analysis as a means of identifying meaningful psychological constructs from a matrix of correlations is introduced from the algebraic and geometric perspectives.
Family History Versus Family Study Methods in Genetics
Authors: Burke Ailbhe , McGuffin Peter 
Published: September 2005

AbstractFamily history methods, where the familiality of a trait or disorder is explored by taking a history from one or more informants, are less costly and time‐consuming than family studies where a direct interview and examination of all available relatives of an index case is carried out. However family studies have a higher sensitivity. Studies based on families alone cannot distinguish between the effects of shared genes and shared environment.
Heuristics: Fast and Frugal
Authors: Hoffrage Ulrich 
Published: September 2005

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.
Fisherian Tradition in Behavioral Genetics
Authors: Dickins David 
Published: September 2005

AbstractR.A. Fisher did much to bring about the ‘Modern Synthesis’ of Mendelian genetics with Darwinian natural selection into a theory of evolution that worked. This was the fruit of his dual talent as a mathematician (particularly, but not exclusively, as a statistician) and a biologist. Though his training also in physics would have led him to welcome the subsequent transformation of parts of biology to ‘hard science’ status in the field of molecular and developmental genetics, his insights were in the domain of transmission genetics, acting in populations, from which he derived a cluster of fecund hypotheses that have been particularly influential in the field of behavioral biology/evolutionary psychology.
Fuzzy Cluster Analysis
Authors: Manton Kenneth G. , Lowrimore Gene , Yashin Anatoli , Kovtun Mikhail 
Published: September 2005

AbstractUsually in cluster analysis, an object is a member of one and only one cluster, a property described as ‘crisp’ membership. Fuzzy cluster analysis allows an object to have partial membership in more than one cluster. Selecting a good membership function is important to the success of the methods. The Grade of Membership model – which has a long history of usage in other contexts – is proposed as a straightforward way of performing fuzzy cluster analysis. The Grade of Membership model is illustrated by an example involving gene expression data.
Game Theory
Authors: Colman Andrew M. 
Published: September 2005

AbstractGame theory models interactive decision making in terms of players, strategies, and payoffs. Conventional (individual) decision theory cannot prescribe rational choice in games, because a player cannot maximize expected utility without knowing how the coplayer(s) will act. Game theory, therefore, incorporates assumptions of common knowledge in addition to rationality. Rational players choose dominant strategies whenever possible, but many games lack such strategies. Every finite game has a Nash equilibrium, and this is the key solution concept of game theory, but the theory is indeterminate, because many games have multiple equilibria. Various criteria for equilibrium selection have been proposed. Experimental games provide information about the strategic behavior of human decision makers with bounded rationality. Evolutionary game theory helps to explain the evolution of cooperation and altruism and adaptive learning in repeated games.
Gene‐Environment Correlation
Authors: Dick Danielle M. 
Published: September 2005

Abstract

Genetic and environmental effects are not independent sources of influence on behavior, but rather, genotype can influence an individual's exposure to certain environments, how that individual experiences the environment, and the degree of influence that certain environments exert. This phenomenon is called
gene‐environment correlation
. Three types of gene‐environment correlation are discussed: passive, evocative, and active. Examples of each type of gene‐environment correlation, and support for these effects from the literature are presented.
Gene‐Environment Interaction
Authors: Jacobson Kristen C. 
Published: September 2005

Abstract
Gene‐environment interaction, or GxE, as it is commonly called in behavioral genetic literature, is the acknowledgement that genetic and environmental influences on variation in behaviors and traits may not act additively and independently, but may instead be dependent upon one another. There are three main strategies for assessing GxE interactions in behavioral genetic research, primarily through the use of adoption studies, twin studies, and studies of genotyped individuals. Each method has its relative strengths and weaknesses.
Generalizability
Authors: Berger Vance W. 
Published: September 2005

Abstract
Researchers generally study a phenomenon in a specific setting, and then try to generalize the obtained results to other settings. The degree to which the results of a study can be generalized from one setting to other settings is referred to as ‘generalizability’. Generalizing information from observations is essential for synthesis of knowledge, and there are some commonly accepted general rules for making generalizations. Here, we will first present a set of such commonly accepted rules and discuss the applications of these rules in behavioral sciences. We will then argue that generalization is not a mechanistic or statistical process, but rather that it requires abstract thinking. Therefore, any set of rules for generalizing information is subject to many exceptions.
Generalizability Theory: Basics
Authors: Boulet John R. 
Published: September 2005

Abstract
Generalizability (G) theory can be thought of as a comprehensive extension of classical test theory (CTT). The focus of G theory is on isolating and estimating the relative magnitude of specific sources of measurement error. This information can be used to optimize the sampling of measurement conditions in future studies. The major concepts underlying G theory, including the relevant terminology and nomenclature, are presented in this entry. The use of G theory is illustrated through data obtained from an assessment of the clinical skills of physicians. On the basis of these data, variance components are estimated, interpreted, and used to calculate reliability‐like coefficients. In addition, various Decision (D) studies are undertaken to provide a basic understanding of how a simple generalizability scenario can be extended to more complex designs, including those in which some sources of unwanted variation in the observations may be considered to be random, fixed, or nested. These studies demonstrate how an investigator can concentrate on targeting and minimizing measurement conditions that contribute the largest amounts of error, thereby increasing generalizability. Generalizability theory provides models and methods that allow an investigator to disentangle multiple sources of error. As a result, it is an important tool for producing reliable, valid, and comparable assessments.
Generalizability Theory: Estimation
Authors: Sanders Piet F. 
Published: September 2005

Abstract
Generalizability theory is a conceptual and statistical framework for the analysis and construction of measurement instruments. Among the most important concepts of generalizability discussed in the first section of this entry are the universe of admissible observations, universe and observed scores, random and fixed facets, crossed and nested designs, variance components, generalizability studies, decision studies, and generalizability coefficients. A discussion of the results of a generalizability study and a decision study of a crossed one‐facet random effects design and of a two‐facet crossed one‐facet random effects design is presented in the two following sections. A number of other designs that illustrate the versatility of generalizability theory are presented in the final section.
Generalized Estimating Equations (GEE)
Authors: Hardin James W. 
Published: September 2005

AbstractCorrelated datasets arise from repeated measures studies where multiple observations are collected from a specific sampling unit (a specific patient's status over time), or from grouped orclustered datawhere observations are grouped based on sharing some common characteristic (animals in a specific litter). When measurements are collected over time, the term longitudinal or panel data is preferred. Generalized estimating equations provide a framework for analyzing correlated data. This framework extends the generalized linear models methodology, which assumes independent data. We discuss the estimation of model parameters and associated variances via generalized estimating equation methodology.
Genotype
Authors: Elston Robert C. 
Published: September 2005

Abstract
The genotype of an organism is its genetic make‐up, as opposed to its phenotype, or outward appearance. If the two alleles at a locus are the same, the genotype is homozygous; otherwise it is heterozygous. If a heterozygote has the same phenotypic distribution as one of the corresponding homozygotes, the allele in that heterozygote is dominant and the other allele is recessive with respect to that phenotype. A multilocus genotype is made up of two haplotypes. In the case of a quantitative trait, dominance corresponds to intralocus interaction, epistasis to interlocus interaction.
Goodness of Fit
Authors: Mulaik Stanley A. 
Published: September 2005

AbstractGoodness of fit is assessed by tests of exact fit and indices of approximation. The chi squared statistic is a test of exact fit. Many indices of approximation are based on estimates of the normalized noncentrality parameter, which is a measure of the degree of lack of fit for the model to the data that is invariant of sample size. Discussed are commonly used indices such as the CFI, the RMSEA, the GFI index, and Steiger's Γ1.
Goodness of Fit for Categorical Variables
Authors: von Eye Alexander , Bogat G. Anne , von Weber Stefan 
Published: September 2005

AbstractThe examination of frequency distributions of categorical variables often involves an evaluation of the appropriateness of probability models.Goodness‐of‐fitstatistics are suitable for this task. These statistics assess the discrepancy between the data and a probability model. Applications focus on (1) overall goodness‐of‐fit and (2) individual residuals. This entry describes the most frequently used goodness‐of‐fit statistics and presents an application in the context of logistic regression.
Graphical Methods Pre‐20th Century
Authors: Smith Laurence D. 
Published: September 2005

AbstractDespite their utility for analyzing and presenting data, the use of graphs in science is a relatively late development and their emergence through history has been uneven and contested. Modern statistical graphics date largely to the pioneering work of William Playfair in the late eighteenth century. The gradual spread of graphical methods in the early nineteenth century was slowed by various antigraph prejudices, but eventually culminated in their enthusiastic reception during the second half of that century. During the Golden Age of graphics, graphs were used extensively in fields ranging from demography to laboratory physiology. Hailed as the universal language of science, graphs underwent a proliferation of novel formats, and they figured crucially in a number of scientific discoveries. The behavioral scientists of that period – including many of psychology's founding figures – made sophisticated applications of graphical methods, but such methods would be eclipsed by the rise of inferential statistics in the behavioral sciences after 1900.
Graphical Presentation of Longitudinal Data
Authors: Wainer Howard , Spence Ian 
Published: September 2005

AbstractThere are some ideas that are so ubiquitous that we cannot imagine the world without them: language and numbers come to mind as immediate examples. Data‐based graphics provide a third example. The graphical representation and communication of data is such a simple and clear process that it is no easier to think of a world before graphs than a world before arithmetic. Yet, graphs were invented and, moreover, invented relatively recently. The very first plots were graphs of longitudinal data. In this essay, we will trace some of the development of graphs for the display of longitudinal data.
Hawthorne Effect
Authors: Gottfredson Gary D. 
Published: September 2005

Abstract

The term
Hawthorne effect
has come to refer mistakenly to an increase in worker performance resulting merely from research or managerial attention to a work group. References to a Hawthorne effect in describing research have also come to connote flawed research. The remarkable improvements in worker productivity in the Hawthorne relay‐assembly experiment probably occurred because workers (a) began to receive pay contingent on the number of relays assembled, (b) set personal goals of increasing their assembly rate, and (c) attended to information about their productivity. The separation of the experimental workers from other assemblers also removed a social obstacle to improved productivity. This led workers to learn to assemble more relays per hour. The Hawthorne researchers intended to study the influence of arrangements that reduced fatigue on productivity, but the study design failed to rule out learning as the mechanism leading to improved performance.
Heteroscedasticity and Complex Variation
Authors: Goldstein Harvey 
Published: September 2005

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.