bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computer‐based Testing
Authors: Davey Tim 
Published: September 2014

Abstract
Most broadly defined, computer‐based tests (CBTs) include not just tests administered on computers or workstations but also exams delivered via telephones, PDAs, and other electronic devices. There have been three main reasons for test developers to move beyond conventional paper and pencil administration. The first is to change the nature of what is being measured. The second is to improve measurement precision or efficiency. The third is to make test administration more convenient for examinees, test sponsors, or both. This entry details some of the advantages of CBTs relative to conventional paper and pencil tests, and describes some of the methods used for test administration and scoring.
Conditional Standard Errors of Measurement
Authors: Tong Ye , Kolen Michael J. 
Published: September 2014

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.
Attitude Scaling
Authors: Johnson Matthew S. , Junker Brian W. 
Published: September 2014

Abstract

Attitude scaling is the process of locating
J
attitude statements or items and/or
N
individuals on a latent continuum or scale. There are numerous methods available for the scaling of attitudes. The earliest methods, developed by L. L. Thurstone, R. Likert, and L. Guttman required two stages: One stage to locate the items on the scale and a second stage to locate the respondents. Other early work on the scaling of attitudes by C. H. Coombs allowed for the simultaneous estimation of both items and respondents, but the assumptions proved too restrictive for most real examples. Modern attitude scaling techniques use item response theory models, both monotone and unfolding, to perform attitude scaling.
Criterion‐Referenced Assessment
Authors: Hambleton Ronald K. , Li Shuhong 
Published: September 2014

Abstract
This entry provides an introduction to criterion‐referenced testing and approaches for resolving several technical problems. Several of the important differences between norm‐referenced testing and criterion‐referenced testing will be introduced first. Next, the challenge of setting performance standards on criterion‐referenced tests and several of the methods will be discussed. Finally, approaches for estimating decision consistency and decision accuracy from single and double test administrations are described.
Differential Item Functioning
Authors: Rogers H. Jane 
Published: September 2014

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.
Item Bias Detection: Classical Approaches
Authors: Mellenbergh Gideon J. 
Published: September 2014

Abstract
Under 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.
Log‐linear
R
asch Models for Stability and Change
Authors: Meiser Thorsten 
Published: September 2014

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.
Classical Test Score Equating
Authors: Kolen Michael J. , Tong Ye 
Published: September 2014

AbstractTest equating procedures are statistical score adjustment procedures that are used when alternate forms of educational and psychological tests are constructed to a common set of content and statistical specifications. Alternate forms enhance the security of tests and allow for examinees to be tested more than one time with a different set of test questions. Even though efforts are made by test developers for the alternate forms to be equivalent, such forms differ somewhat in difficulty. A data collection design is necessary for conducting equating. Equating also involves application of statistical methodology. Traditional equating methods equate score distributions. In this entry, equating designs and traditional equating methods are described. Error in equating is considered. Selected practical issues are also discussed.
Maximum Likelihood Item Response Theory Estimation
Authors: Rupp André A. 
Published: September 2014

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.
Item Bias Detection: Modern Approaches
Authors: Mellenbergh Gideon J. 
Published: September 2014

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.
Facet Theory
Authors: Borg Ingwer 
Published: September 2014

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.
T
au‐Equivalent and Congeneric Measurements
Authors: F. Ten Berge Jos M. 
Published: September 2014

Abstract
To evaluate the reliability of a test, one needs a parallel test. When parallel tests are absent, one option is to rely on hypotheses such as that the test parts are essentially tau‐equivalent or congeneric, having only one factor in factor analysis. Such hypotheses are unrealistic, except that a test may be congeneric when there are only three test parts; in that case, however, it is impossible to decide what the number of factors really is. A preferable option is to use lower bounds to reliability, such as coefficient alpha. The Jackson & Agunwamba greatest lower bound (glb) is better than alpha but has a sampling bias problem. Bias correction methods are still under study. Nevertheless, when compared to reliability estimates derived from the hypothesis of congeneric tests, the glb is already to be preferred, because these estimates behave very similarly to glb, and display the same sampling bias but do not rely on the hypothesis of a congeneric test
Hierarchical Item Response Theory Modeling
Authors: Bolt Daniel , Kim Jee‐Seon 
Published: September 2014

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.
Item Response Theory Models for Polytomous Response Data
Authors: Keller Lisa A. 
Published: September 2014

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 Models for Rating Scale Data
Authors: Engelhard George 
Published: September 2014

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.
Test Construction
Authors: Gierl Mark J. 
Published: September 2014

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.
Person Misfit
Authors: Meijer Rob R. 
Published: September 2014

Abstract
Methods are discussed that can be used to investigate the fit of an item score pattern to a test model. Person‐fit statistics that are formulated in the context of item response theory(IRT) take a prominent place in the literature. Also, methods formulated outside the IRT context, and methods investigating particular types of response behavior are discussed. Finally, the usefulness of person‐fit statistics to solve practical measurement problems is considered.
Parsimony/Occham's Razor
Authors: Mulaik Stanley A. 
Published: September 2014

Abstract
In modeling data, parsimony is the fewness of model parameters estimated relative to the number of data points fit by the model. The more parsimonious a model, the more degrees of freedom by which the model can fail to fit the data, thus making the model more potentially disconfirmable and more strongly supported when the model fits.
Battery Reduction
Authors: Massaro Joseph M. 
Published: September 2014

Abstract

Battery reduction is used to select a subset of
m
variables from an original set of
n
variables (
m
<
n
) that reproduce a large proportion of the variance in the original set of
n
variables. There are a number of procedures for performing battery reduction analysis. A popular method involves performing a principal components analysis first to select
m
components, which account for the salient variance in the original data. Gram–Schmidt orthogonal rotations are then performed to determine the
m
variables that account for the largest proportion of variance. The procedure is illustrated and reference made to a SAS macro for performing the analysis.
Covariance Structure Models
Authors: Thum Y. M. 
Published: September 2014

Abstract
As originally formulated by Bock and by Bock and Bargmann, covariance structure analysis is a statistical method for the structural analysis of the sample variance–covariance matrix of respondent scores on a test battery aimed at validating the constructs making up the battery. A generalization of the mixed‐effects model, we examine some of its applications beyond tests and testing. We provide revised results for the data analyzed by Wiley, Schmidt, and Bramble. We note its formal equivalence with a confirmatory factor analysis with known factor loading and note some of its connections to other statistical models for multivariate outcomes.
Second Order Factor Analysis: Confirmatory
Authors: Tisak John , Tisak Marie S. 
Published: September 2014

Abstract
Confirmatory second‐order factor analysis may be conceptualized as a further factor analyzing of the results of an initial factor analysis of some observed variables. Specifically, the initial or first‐order factor means, variances, and covariances are re‐analyzed into more general or abstracted variables. For pedagogical reasons, this approached is initially developed via a first and second‐order multivariate multiple regression analysis using measured or observed variables. Next by analogy, the approach is extended to latent variates or unobserved factors. Throughout an illustration is presented using Thurstone's Primary Mental Abilities, Horn and Cattell's Fluid and Crystallized Intelligences, and Spearmen's General Intelligence.
C
holesky Decomposition
Authors: Cherny Stacey S. 
Published: September 2014

Abstract
The Cholesky decomposition is probably the most commonly used model in behavior genetic analysis. Referring to it as a model, however, is somewhat misleading, since it is, in fact, primarily a method for estimating a covariance structure under the constraint that the estimated covariance matrix is positive semidefinite. The specific parameter estimates of the decomposition are of less interest than the expected covariance matrix it implies.
Structural Equation Modeling: Overview
Authors: Schumacker Randall E. 
Published: September 2014

AbstractStructural equation modeling (SEM) software programs analyze theoretical models using a sample variance–covariance matrix, although a correlation matrix with means and standard deviations for the observed variables can be input because these values can be used to create a variance–covariance matrix. A LISREL‐SIMPLIS program is given below to illustrate how the theoretical model and sample data are included in a SEM program.
Three‐Mode Component and Scaling Methods
Authors: Kroonenberg Pieter M. 
Published: September 2014

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.
Factor Analysis: Multitrait–Multimethod
Authors: Wothke Werner 
Published: September 2014

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.
Data Mining
Authors: Hand David J. 
Published: September 2014

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.
Multivariate Genetic Analysis
Authors: Gillespie Nathan A. , Martin Nicholas G. 
Published: September 2014

Abstract
The goal of multivariate genetic analysis is to estimate the causes of genetic and environmental covariation between phenotypes. As with univariate modeling, when data from genetically informative relatives such as twins are available, the genetic and environmental components of covariation can be separated by a technique which is analogous to factor analysis (Martin & Eaves,). This is achieved by making use of the additional information in the cross‐correlations between relatives for different traits (or same traits measured at different times) and can allow researches to estimate the extent to which genetic and environmental influences are shared in common by several traits or are trait specific. The various forms of multivariate analyses covered in this section include the Cholesky decomposition, common and independent genetic pathway models, and genetic simplex modeling. This section will give the reader an appreciation for the flexibility of SEM approaches to address more complicated questions beyond simpler univariate decompositions.
Structural Equation Modeling: Latent Growth Curve Analysis
Authors: Willett John B. , Bub Kristen L. 
Published: September 2014

Abstract
In this chapter, we use an example of panel data on the longitudinal development of reading in children to introduce the latent growth curve analysis. Latent growth modeling originates in a simple mapping of the standard multilevel model for change onto the general model available for covariance structure analysis (CSA). Here, we illustrate how the latent growth modeling approach can be enacted by using the CSA Y‐measurement model to contain the level‐1 individual growth model and the CSA structural model to contain the level‐2 model for interindividual differences in change. Because this mapping is possible, all fixed and random parameters in the multilevel model for change can be estimated by fitting the corresponding CSA model to data using standard structural equation modeling software (such as LISREL). Latent growth curve analysis is important because it capitalizes on the intrinsic flexibility of the general CSA model to test interesting and complex hypotheses about change, many of which are either more difficult or impossible to test using standard multilevel modeling approaches.
Factor Analysis of Personality Measures
Authors: Chaplin William F. 
Published: September 2014

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.
Cluster Analysis: Overview
Authors: Manton Kenneth G. , Lowrimore Gene , Yashin Anatoli , Kovtun Mikhail 
Published: September 2014

AbstractThis article discusses analysis techniques for clustering objects into hopefully meaningful sets. Hierarchical methods are presented for clustering both variables and cases. Examples are presented for variations on the methods. A nonhierarchical method, k‐means, is discussed for continuous data. Finally, an example comparing the results of k‐means clustering to an ad hoc method using principal components analysis is given.