bluuebunny/crossref_metadata_2025_split huggingface dataset:
Telephone Surveys
Authors: Driskell Robyn Bateman 
Published: September 2005

Abstract
Telephone survey methodology is often the preferred approach to surveying as it is a valid and reliable means of collecting data. The advantages of telephone surveys include cost efficiency, reduced survey time, and the ability to reach a large, representative sample. With the recent technologies of random‐digit dialing and computer‐assisted telephone‐interviewing, telephone survey methodologies have enhanced and improved. While some limitations exist in telephone surveys, the advantages outweigh the disadvantages, resulting in an efficient and effective method for collecting data.
Test Bias Detection
Authors: Beller Michal 
Published: September 2005

AbstractMuch of the criticism of psychological tests is derived from the observation that certain groups differ extensively in test performance. However, much of the polemic concerning the issues of test bias rests on two main areas of confusion: (a) differences in test performance among groups are often regarded, in and of themselves, as an indication of test bias, ignoring performance on the external criterion that the test is designed to predict. Often, groups that perform poorly on tests also tend to perform poorly on measures of the criterion. Furthermore, analysis of the relationship between tests and criteria often reveals similar regression lines for the various groups; and (b) the issue of test bias is often confused with the possibility of bias in the content of some individual items included in a test. Group differences in test performance are attributed to the content of specific items, and rather than eliminating problematic items in a systematic way (i.e., checking all items for differential performance), this confusion has, in some cases, led to suggestions of a wholesale rejection of reliable and valid test batteries.
Test Construction
Authors: Gierl Mark J. 
Published: September 2005

Abstract
Item response theory (IRT) provides a method to determine the amount of psychometric information each item (i.e., item information function) and combination of items (i.e., test information function) provides in the estimation of an examinee's ability. In a 1977 paper, Lord described a basic four‐step procedure for using these functions to construct a test using calibrate items from a bank. Lord's procedure requires specifying a target function, selecting items from the bank to fill this target, adding the item information functions together during the selection process, and continuing until the test information function approximates the target function. Some key issues related to specifying the target function and selecting items are presented and discussed. More recent developments in test construction using item and test information functions are also described.
Independence: Chi‐square and Likelihood Ratio Tests
Authors: Brown Bruce L. , Hendrix Kent A. 
Published: September 2005

Abstract

Chi‐square is the most common and simplest test of independence. It is a nonparametric test intended for use with categorical data. The conceptual basis for testing independence with a chi‐square analysis is closely related to the rationale for using F tests in the analysis of variance to test interactions among two or more independent variables. Indeed, the
general linear model
that applies to analysis of variance can also be applied as a logarithmic linear analysis of categorical data. These
log‐linear models
are a more general, elegant, and sophisticated way of testing independence based upon the
general linear model
.
Tetrachoric Correlation
Authors: Hershberger Scott L. 
Published: September 2005

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.
Three Dimensional (3
D
) Scatterplots
Authors: Wright Daniel B. , Williams Siân E. 
Published: September 2005

Abstract
A three‐dimensional scatterplot displays the relationship between three continuous variables at once. The data are represented as a cloud of points within the three‐dimensional space formed by the three axes of the plot. Such plots can be difficult to interpret and other methods of representing the third variable are generally more effective.
Three‐Mode Component and Scaling Methods
Authors: Kroonenberg Pieter M. 
Published: September 2005

Abstract
In this entry, the basic principles and rationale for three‐mode analysis of three‐way data are introduced. The wealth of the three‐mode world is sketched, and the power of its methods is illustrated with a three‐mode component analysis analyzing how children cope in several school‐related situations and an individual differences scaling analysis of the perception of pain.
Time Series Analysis
Authors: Brockwell P. J. 
Published: September 2005

AbstractThe time‐domain approach to the modeling and forecasting of time series is outlined. Weakly stationary processes are introduced, and an introduction to ARMA processes and their properties is given. Estimation, model selection, and prediction with these models are considered, and extensions to multivariate time series, state‐space models and nonlinear models are indicated.
Trellis Graphics
Authors: Everitt Brian S. 
Published: September 2005

Abstract

In trellis graphics two or more variables are plotted for (conditioned on) different values or categories of a given variable (or variables). Component graphs for each level of the selected variable are arranged in a single display in order to make comparisons. Many different types of plot lend themselves to this treatment, including
scatterplots
and
three‐dimensional scatterplots
. Trellis graphics are helpful help in understanding both the structure of the data and how well proposed models for the data actually fit.
Trend Tests for Counts and Proportions
Authors: Liu Li , Berger Vance W. , Hershberger Scott L. 
Published: September 2005

Abstract
In a contingency table, one may be interested in whether there is a trend along the row variable or the column variable. In an R by 2 or 2 by C contingency table, where one variable is binary and the other is ordinal, the Cochran–Armitage test for trend can be used to test whether the binomial proportions have a linear trend across the ordinal variable. In an R by C contingency table, where one variable is ordinal and the other variable is nominal, the Jonckheere–Terpstra trend test can used to determine whether, within each level of the nominal variable, the proportions are distributed identically.
Twin Designs
Authors: Spinath Frank M. 
Published: September 2005

Abstract

The classical twin study compares the phenotypic resemblances of identical or
monozygotic
(MZ) and fraternal or
dizygotic
(DZ) twins. MZ twins derive from the splitting of one fertilized zygote and, therefore, inherit identical genetic material. DZ twins are first‐degree relatives because they develop from separately fertilized eggs and are 50% genetically identical on average. It follows that a greater within‐pair similarity in MZ, compared to DZ twins suggests that genetic variance influences the trait under study.
Two by Two Contingency Tables
Authors: Liu Li , Berger Vance W. 
Published: September 2005

Abstract

Two by two contingency tables are generated by cross classifying subjects by two binary variables. In such tables, one may be interested in testing for an association between the two variables, and, if it is found, then also in estimating the strength of the association. The χ
2
test of independence and Fisher's exact test are illustrated to test independence. The difference in proportions, relative risk, and odds ratio are shown to measure the association. Sensitivity, specificity, the false positive rate, and the false negative rate are used to evaluate a test method. Simpson's paradox is introduced to illustrate the difference between marginal tables and partial tables.
Two‐Way Factorial: Distribution‐Free Methods
Authors: Toothaker Larry E. 
Published: September 2005

AbstractNonparametric and resampling approaches for the two‐way factorial design include permutation tests, bootstrap tests, the rank transform, aligned ranks tests, tests on trimmed means, and a rank‐based ANOVA method that allows heteroscedastic variances, one known as the BDM method, or combinations of these such as bootstrap tests on trimmed means. Research shows the BDM method, parametric tests on trimmed means, and bootstrap tests on trimmed means to be the most promising methods in terms of α‐control and power.
Utility Theory
Authors: Read Daniel , Airoldi Mara 
Published: September 2005

AbstractThe term utility is used to denote either the quantity of pleasure or pain arising from an outcome, or the preferences over outcomes reflected in choice behavior. The first use of the term is associated with utilitarian philosophers of the last century, and the second with twentieth‐century economists. Recent developments in psychology and behavioral economics have attempted to reconcile both uses of the term.
Validity Theory and Applications
Authors: Sireci Stephen G. 
Published: September 2005

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.
W
ilcoxon–
M
ann–
W
hitney Test
Authors: DePuy Venita , Berger Vance W. , Zhou YanYan 
Published: September 2005

Abstract

The Wilcoxon‐Mann‐Whitney test evaluates the difference in medians between two similarly shaped populations, which have the same variance. This nonparametric test is similar to the two‐sample Student's
t
Test. The primary differences are that the
t
Test requires the assumption of normality, while the Wilcoxon‐Mann‐Whitney test can be performed where only rankings, that is, ordinal data, are recorded. While exact computations of the test statistic are possible, a large sample approximation is often used for ease of calculation. Both methods are available in various commercial statistical packages.
Within Case Designs: Distribution Free Methods
Authors: Lix Lisa M. , Keselman H. J. 
Published: September 2005

AbstractIn within‐case designs, subjects are measured repeatedly over time or across multiple experimental conditions. Univariate and multivariate parametric procedures for testing within‐case effects may lose statistical power under departures from a multivariate normal distribution. Nonparametric procedures based on rank scores or bootstrapping are useful alternatives for testing within‐case effects when the data are skewed or have heavy tails.
Yates' Correction
Authors: Stefanescu Catalina , Berger Vance W. , Hershberger Scott L. 
Published: September 2005

AbstractYates' correction for continuity adjusts the formula for Pearson's chi‐square test by subtracting 0.5 from each observed value in a 2 × 2 contingency table. This formula is used when at least one cell of the table has an expected frequency less than 5, although some sources say that this correction should be used when the expected frequency is less than 10.
Social Networks
Authors: Wasserman Stanley , Pattison Philippa , Steinley Douglas 
Published: September 2005

Abstract

Network analysis is the interdisciplinary study of social relations and has roots in anthropology, sociology, psychology, and applied mathematics. It conceives of social structure in relational terms, and its most fundamental construct is that of a
social network,
comprising at the most basic level a set of
social actors
and a set of
relational ties
connecting pairs of these actors. The
nodes
or
members
of the network can be groups or organizations as well as people. Network analysis involves a combination of theorizing, model building, and empirical research, including (possibly) sophisticated data analysis. The goal is to study network structure, often analyzed using such concepts as density, centrality, prestige, mutuality, and role.
Sequential Decision Making
Authors: Vos Hans J. 
Published: September 2005

Abstract
The purpose of this entry is to describe optimal rules for sequential mastery tests in the context of education. In a sequential mastery test, the decision is to classify a student as a master, a nonmaster, or to continue testing and administering another random item. The framework of Bayesian sequential decision theory is used; that is, optimal rules are obtained by minimizing the posterior expected losses associated with all possible decision rules at each stage of testing. The main advantage of this approach is that costs of testing can be taken explicitly into account. For given maximum number of items to be administered, it is shown how the appropriate action can be computed at each stage of testing for different number‐correct score.
Hierarchical Item Response Theory Modeling
Authors: Bolt Daniel , Kim Jee‐Seon 
Published: September 2005

AbstractThe synthesis of hierarchical modeling with item response theory (IRT) has led to psychometric models for item response datasets that possess hierarchical structure. In this entry, an overview is provided of hierarchical IRT methodology and its usefulness as a general framework both for traditional IRT models and new extensions. Models for both random person and random item effects are discussed.
Factor Score Estimation
Authors: Hershberger Scott L. 
Published: September 2005

AbstractFactors scores are measures of principal components or common factors. Under the principal components model, the factor scores are uniquely determined; under the common factor model, they are not. In the latter situation, the factor scores are indeterminate, potentially having an infinite number of solution sets, and thus their true values can only be estimated. Three methods of factor score estimation are discussed: (a) regression, (b) Bartlett's method, and (c) Anderson and Rubin's method. Although the properties and values of the factor score estimates produced by the three methods differ, the estimates are in general highly correlated.
New Item Types and Scoring
Authors: Drasgow Fritz , Mattern Krista 
Published: September 2005

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.
M
artingales
Authors: Laming Donald 
Published: September 2005

Abstract
The concept of a martingale is explained and illustrated with several examples from the psychological literature. Martingales are also compared with the related notion of a random walk. It is shown that the variance of successive sums in a martingale is monotonically increasing and the theorem is quoted that, if the variance is bounded, the distribution of the sum tends asymptotically to a limit.
Probability: Foundations of
Authors: Macdonald Ranald R. 
Published: September 2005

AbstractProbability and probability models are mathematical representations of uncertainties. They can be seen either as analogies with urn models or as a logical system which conforms to a set of probability laws or axioms. It is argued that if uncertainties conform to an urn model or to the probability axioms and the probability space is dense enough then there is a correct probability associated with every uncertainty. Because probability laws place constraints on uncertainties there are disputes over where probabilities apply. One view is that probabilities are relative frequencies. This means that probabilities do not apply to unique events as it might be necessary to break down any relative frequency further before it can be applied to a particular event. Nevertheless some conformity between people's judgments and the frequency view of probability are reported. An alternative subjective approach takes probabilities to be measures of belief but it is unclear then why they should conform to probability laws. A partial answer is that if a person X bets in accordance with subjective probabilities which violate these laws then X can be made to lose regardless of the state of the world. Problems with taking probabilities to be measures of belief are discussed and it is concluded that neither the frequency nor the subjective approaches provide absolute standards against which people's uncertainty judgments should be compared. Theorists have attempted to identify objective probabilities but it is unclear how their existence could ever be established. Nevertheless the search for objective probabilities is defended.
Social Interaction Models
Authors: Hewitt John K. 
Published: September 2005

Abstract
Social interaction is modeled in behavior genetics as the influence of one individual's phenotype on another. The consequences of this for phenotypic variances and covariances among relatives of different degrees of relationship can be predicted from an extension to the usual path models. Social cooperation (or imitation) and social competition (or contrast) result in different patterns of variation and resemblance that depend on the prior similarity of the interacting pair. Although of considerable theoretical interest, the behavior genetics literature does not offer many convincing examples of the empirical effects of such social interactions.
Binomial Test
Authors: Wagner‐Menghin Michaela M. 
Published: September 2005

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.
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. From a statistical standpoint, the main feature of clustered data that needs to be accounted for in any analysis is the fact that units from the same cluster are likely to respond in a more similar manner than units from different clusters. As a consequence, clustered data may not contain quite as much information as investigators might otherwise like to believe it does.
Survival Analysis
Authors: Landau Sabine 
Published: September 2005

AbstractSurvival analysis is the study of the distribution of event times (death, relapse, completion of an experimental task). A distinguishing feature of survival or time to event data is the presence of censoring, that is, all that is known sometimes is that an event occurred later than a given point in time. This article provides a brief description of the classical methods for nonparametric, semiparametric and fully parametric analysis of survival times.
Item Bias Detection: Modern Approaches
Authors: Mellenbergh Gideon J. 
Published: September 2005

Abstract
A general definition of differential item functioning (DIF) is presented. The definition is specified for DIF between two groups and a test (questionnaire) that measures one latent trait. Methods for DIF detection are described for dichotomous, ordinal‐polytomous, and continuous item responses under unidimensional item response theory (IRT) models.
Classical Statistical Inference Extended: Split‐Tailed Tests
Authors: Harris Richard J. 
Published: September 2005

Abstract
Two‐tailed tests of statistical significance do not take into account the strength of the evidence that existed before the study under investigation was conducted with respect to the sign of the population effect being tested. The split‐tailed test incorporates this prior evidence by assigning a greater proportion of alpha to the rejection region corresponding to the direction of effect most strongly supported by the prior evidence. In addition to providing greater power than a two‐tailed test when the researcher's assessment of the prior evidence is correct (but much lower power to detect the correct direction of effect when the prior evidence has led to the wrong expectation), split‐tailed tests also make clear that one‐tailed tests represent the extreme case of infinite bias in favor of one's prior hypothesis about the sign of the population effect and, thus, violate the principle that scientific hypotheses must be disconfirmable.
Nonparametric Correlation (

r

s

)
Authors: Howell David C. 
Published: September 2005

Abstract

Spearman's correlation coefficient (
r

S

) for ranked data, often known as Spearman's rho, actually represents the standard Pearson product‐moment correlation applied to ranked data. Data on
X
and
Y
are replaced by their ranks, and then the correlation between the ranks is computed.
Classification and Regression Trees
Authors: Everitt Brian S. 
Published: September 2005

Abstract
Multiple regression, logistic regression and other members of the generalized linear model family are widely used but may not always give a faithful description of the data when the assumptions on which they are based are not met, or in the presence of higher‐order interactions among some of the explanatory variables. An entirely different approach to explaining the relationship between a response variable and a set of explanatory variables that has been developed to overcome some of these possible problems is the classification and regression (CART) procedure, which extracts subgroups of observations within which the explanatory variables are relatively homogeneous, and between which the response variable is relatively distinct. Creation of subgroups takes place according to a tree structure, in essence a series of binary splits. Often the resulting tree is readily interpretable and offers insight into the structure and pattern in the data.
Heritability: Overview
Authors: Rose Richard J. 
Published: September 2005

Abstract
Heritability, a statistical measure originating in quantitative genetics, is an estimate of the contribution of genetic differences to the differences observed in a measured variable (e.g., some dimension of behavior) in a given population at a given time. Heritability is a population concept. It is an estimate, typically made from the resemblance observed among relatives. It does not describe individuals; it refers only to the underlying differences between people.