bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonlinear Models
Authors: Goldsmith L. Jane , Berger Vance W. 
Published: September 2005

Abstract
Nonlinear models relate a response or dependent variable to one or more predictor variables. The relationship is more complicated than that of the familiar straight‐line linear model, although the least‐squares error criterion is usually used for estimation. The methodology of nonlinear models is especially valuable when special relationships among response and predictor variables are known, resulting from solutions of differential equations or graphical interpretations. Estimates must be found through a complicated iterative process, which can fail to converge. Choice of a model is critical not only for adequate description of the nonlinear relationship but also to obtain possible advantageous estimation properties.
Nonparametric Item Response Theory Models
Authors: Sijtsma Klaas 
Published: September 2005

Abstract
Nonparametric item response theory serves two purposes. The first is to provide a framework for flexible, exploratory analysis of item scores. Several procedures are available for investigating the dimensionality of test data. Estimated item response functions are checked for monotonicity and other order restrictions. Nonparametric item response models imply ordinal scales for persons and items. Ordinal scales are sufficient for many practical applications in psychology, education, sociology, and so on. The second purpose is to provide a mathematical framework for studying the potentials and the limitations of item response theory. Observable data properties implied by nonparametric item response theory may also be valuable for fitting parametric models, and relationships between item response models may be clarified. Present research in nonparametric IRT involves preference data analysis and Bayesian methods for model fitting.
Nonparametric Regression
Authors: Fox John 
Published: September 2005

AbstractNonparametric regression analysis traces the dependence of a response variable on one or several predictors without specifying in advance the function that relates the predictors to the response. This article discusses several common methods of nonparametric regression, including kernel estimation, local polynomial regression, and smoothing splines. Additive regression models and semiparametric models are also briefly discussed.
Nonresponse in Sample Surveys
Authors: Levy Paul S. , Lemeshow Stanley 
Published: September 2005

Abstract
This expository article discusses issues related to unit nonresponse in sample surveys including various definitions commonly used to report response rates; effects of nonresponses on the validity and reliability of estimates; strategies used to improve response rates in sample surveys; and statistical methods used to adjust for unit nonresponse in analyzing data from sample surveys. References are given to ‘classic’ as well as recently published books on this topic.
Nonrandom Samples
Authors: Wainer Howard 
Published: September 2005

Abstract
Drawing causal inferences from observational studies in which the data are not generated from a process of random allocation to treatment yields the often‐likely outcome of substantial nonrandom error. In this essay, we illustrate several examples of the kind and magnitude of such errors that have been made in the past and indicate how, if random assignment to treatment is not possible, we can at least estimate the level of our uncertainty.
Nonshared Environment
Authors: Towers Hilary , Neiderhiser Jenae M. 
Published: September 2005

Abstract
Findings from decades of research show that the environmental experiences of individuals reared in the same home can make them different from one another as well as similar. This is true for a wide range of individual characteristics and personal relationships. The phenomenon in which environmental influences cause family members to differ from one another is known in the behavioral genetic literature as nonshared environment.
Number Needed to Treat
Authors: Lesaffre Emmanuel 
Published: September 2005

Abstract
The number needed to treat (NNT) is the inverse of the difference between two probabilities. Clinicians prefer the NNT over classical measures like the odds‐ratio to evaluate the impact of a new treatment versus a control treatment. On the other hand, statisticians have pointed out that the NNT has undesirable statistical properties. Despite the dispute between clinicians and statisticians, the NNT is becoming increasingly important in Evidence‐based Medicine. The use of NNT will be illustrated with a medical example. Further, we will review the claims that have been made by the clinicians and the criticism of the statistical community.
Odds and Odds Ratios
Authors: Rudas Tamás 
Published: September 2005

Abstract
After defining the concepts of odds, logits, conditional odds, and odds ratios, the asymptotic variances of the estimates of some of these quantities are given. The properties of odds ratio as a measure of association, and among these the importance of variation independence from the marginal distributions, are discussed. The reasons and correct interpretation of the possibly different directions of marginal and conditional associations (Simpson's paradox) are described. Conditional odds ratios are used to give an interpretation of log‐linear models.
One Way Designs: Nonparametric and Resampling Approaches
Authors: Hayes Andrew F. 
Published: September 2005

Abstract
Resampling‐based variants of the one‐way analysis of variance (ANOVA) are discussed, including their assumptions, computation, and interpretation. Although resampling approaches to comparing groups offer some appealing advantages to ANOVA, they are not a panacea to assumption violations. Caution must be exercised in the interpretation of a resampling version of ANOVA because the null hypothesis tested depends on the test statistic and the method of resampling used.
Directed Alternatives in Testing
Authors: Cohen Arthur 
Published: September 2005

Abstract

Consider a hypothesis‐testing problem concerned with a one‐dimensional parameter θ. In testing a null hypothesis
H
: θ = θ

o

, a two‐sided alternative is
K
: θ ≠ θ

o

. A one‐sided alternative is
K
: θ > θ

o

. This latter alternative is appropriate when it is reasonably certain that θ cannot be less then θ

o

. The advantage of posing a one‐sided alternative is that the appropriate one‐sided test is much more powerful than the corresponding two‐sided test. The idea of one‐sided and two‐sided alternatives extends to the case of testing a
k
‐dimensional parameter
θ
. If the null hypothesis is
H
:
θ
=
θ
o
, then the analogue to the two‐sided alternative is
K
:
θ
≠
θ
o
. This latter alternative is said to be unrestricted or not directed. An example of the analogue to the one‐sided alternative is
K
: θ

i

≥ θ

io

, for
i
= 1, …,
k
, with strict inequality for at least one index
i
. Here
θ
= (θ
1
, …θ

k

). Such an alternative is called a
directed alternative
.

We introduce the notion of directed alternatives for multidimensional parameters. Among the special cases treated are the simple order alternative in which parameters are nondecreasing, the tree order alternative in which case one mean parameter is assumed to be less than or equal to each of the other mean parameters, and the stochastic order alternative in which one multinomial distribution is more concentrated at higher categories than another distribution. We indicate appropriate tests for these directed alternatives.
Optimal Design for Categorical Variables
Authors: Berger Martijn P. F. 
Published: September 2005

AbstractOptimal design research is important for behavioral research because it enables efficient estimation of parameters and maximum power with minimum sample size and costs of performing a study. In this paper, an elementary introduction to the concepts of optimal design research is given and illustrated by means of four different cases, namely, linear and logistic models, with and without dummy coding. Although different models have different optimal designs, a rule‐of‐thumb may be that if the independent variables are treated as being quantitative, efficiency will increase if the variance of the independent variable increases, and if the design is as orthogonal as possible. For qualitative independent variables, a design that has equal sample sizes for all categories is often optimal.
Optimal Scaling
Authors: Takane Yoshio 
Published: September 2005

Abstract
A data matrix typically represents some kind of relationship between row and column entities. The relationship represented by the data may be described by a model presumed to have generated the data. Observed data, on the other hand, may be measured on one of a variety of scale levels: nominal, ordinal, interval, or ratio. In such cases, we may attempt to do two things simultaneously: (a) we transform the data by a transformation appropriate for the scale level, and (b) we fit a model to the transformed data to account for the data. This process of simultaneous data transformations and data representations is called optimal scaling. In this article, we briefly discuss some of the key features of optimal scaling.
Optimization Methods
Authors: Lewis Robert Michael , Trosset Michael W. 
Published: September 2005

Abstract
Optimization is concerned with identifying the best element in a set of competing possibilities. Except in special circumstances, one must use numerical methods to search for solutions iteratively. If one's problem is well‐behaved, for example, the objective to be minimized is smooth and the feasible possibilities are smoothly defined by finite numbers of equality and inequality constraints, then one can use a number of sophisticated methods to identify local solutions, that is, possibilities that are as good or better than any other nearby possibilities. However, finding global solutions is much harder.
Paired Observations, Distribution Free Methods
Authors: Berger Vance W. , Li Zhen 
Published: September 2005

AbstractData are said to be paired if theith observation on the first sample is naturally paired with theith observation on the second sample. The objective in paired observations is to eliminate, to the extent possible, sources of extraneous variation. In behavioral studies, for example, paired observations may be employed to assess the effectiveness of a treatment or experimental procedure. Techniques often used to analyze paired observations include the McNemar Chi‐square test, the pairedtTest, the sign test, and the Wilcoxon Signed‐Rank test.
Paradoxes
Authors: Berger Vance W. , Durkalski Valerie 
Published: September 2005

AbstractA paradox is a statement that apparently derives self‐contradictory conclusions. While the consideration of paradoxes in statistics can be instructional, they are also important, because they often have implications that should be kept in mind when designing studies and analyzing data. This entry reviews some of the more common paradoxes encountered in statistics, and their implications.
Partial Correlation Coefficients
Authors: Brown Bruce L. , Hendrix Suzanne B. 
Published: September 2005

Abstract
Partial correlation is a method for identifying the correlation between two variables, with the effects of a third variable held constant. It is mathematically equivalent to the correlation between the residual scores of the two variables regressed upon the control variable. Partial correlation is conceptually related to, and an integral part of, a number of other useful statistical methods, including path analysis, structural equation modeling, factor analysis, and stepwise multiple regression.
Partial Least Squares
Authors: Sampson Paul D. , Bookstein Fred L. 
Published: September 2005

Abstract
Partial Least Squares (PLS), as described in this article, is a method of statistical analysis for characterizing and making inferences about the relationship(s) among two or more causally related blocks of variables in terms of pairs of latent variables. The methods of computation and inference for 2‐block PLS analysis are described and illustrated for an application in behavioral teratology relating a block of 9 subscale scores on the WAIS‐R IQ test measured on 419 subjects to a block of 13 measures of prenatal maternal alcohol consumption.
Path Analysis and Path Diagrams
Authors: Boker Steven M. , McArdle John J. 
Published: September 2005

Abstract
Path analysis refers to the calculation of moment structures within and between variables, for example, covariances or correlations, implied by a set of simultaneous linear regression equations – one type of structural equation model. Path diagrams of structural equation models offer a graphical representation with exact one‐to‐one mapping to the simultaneous linear regression equations. Path analysis allows one to decompose the covariance between two variables in a structural equation model into additive components, thus helping to understand how the interrelationships between many variables in a model predict the covariance between two selected variables. By performing path analysis calculations on all variables and pairs of variables in a model, one may calculate the expected moment structure implied by that model. Path analysis preceded and led to the development of modern structural equation modeling methods of parameter estimation.
Pattern Recognition
Authors: Kuncheva Ludmila I. , Whitaker Christopher J. 
Published: September 2005

Abstract

Pattern recognition concerns assigning objects to classes. The objects are described by features (variables or measurements) organized as
p
‐dimensional points in some feature space. A classifier is a formula, an algorithm or a technique that can assign a class label to any given point in the feature space. Pattern recognition comprises supervised learning (predefined class labels) and unsupervised learning (unknown class labels). Supervised learning includes choosing a classifier model, training and testing the classifier using a data set and selecting the relevant features. Unsupervised learning is usually approached by cluster analysis. Multiple classifier systems or classifier ensembles are a recent branch of pattern recognition whereby the outputs of several classifiers (regarded as experts) are combined for improved accuracy.
P
earson Product Moment Correlation
Authors: Kornbrot Diana 
Published: September 2005

Abstract

Pearson's product moment correlation,
r
, describes the strength of the linear relation between two metric variables, such as extraversion and intelligence. Values range from −1 to +1. A scatterplot is necessary to interpret
r
. The proportion of variability accounted for by the linear relation is given by
r
2
.
Linear Models: Permutation Methods
Authors: Cade Brian S. 
Published: September 2005

Abstract
Permutation tests of hypotheses about parameters in linear models derive their validity from exchangeability of the errors in the null model. These errors are unobservable but differ from the observable residuals by a constant for any hypothesis that posits a null, reduced parameter model with just an intercept term. For these hypotheses, exact permutation inferences are possible for iid error distributions of any form. Other hypotheses on subsets of parameters in multiple regression models are not exact because the residuals are not exchangeable with equal probability. Several options to improve exchangeability for these hypotheses have been developed and are presented for permutation tests on quantile regression estimates of the linear model.
Person Misfit
Authors: Meijer Rob R. 
Published: September 2005

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.
P
itman Test
Authors: Lunneborg Clifford E. 
Published: September 2005

Abstract
The two‐group Pitman Test, proposed by E. J. G. Pitman in 1937, is one of the earliest nonparametric or distribution‐free tests. The test has applications both to random samples drawn independently from two (large) populations, and to the randomization of available cases between two treatments. Although the mechanics of the two applications are identical, the hypotheses tested and the interpretation of those tests differ markedly between the two applications.
Point Biserial Correlation
Authors: Kornbrot Diana 
Published: September 2005

Abstract

The point biserial correlation is the value of Pearson's product moment correlation when one of the variables is dichotomous and the other variable is metric. Values range from +1, a perfect positive relation; through zero, no association at all; to −1, a perfect negative correlation. The square of this correlation,

, is a measure of effect size in terms of the proportion of variability accounted for by the relation between the two variables.
Polychoric Correlation
Authors: Hershberger Scott L. 
Published: September 2005

Abstract

The polychoric correlation is used to estimate the Pearson product‐moment correlation between two continuous, bivariate‐normally distributed variables from categorized versions of those variables. The polychoric correlation may also be thought of as the correlation between the normal scores
Y

i

*,
Y

j

* as estimated from the category scores
Y

i

,
Y

j

. These normal scores are based on estimated threshold values that represent transitions from one category to the other.
Power
Authors: Howell David C. 
Published: September 2005

AbstractThe power of a test refers to the probability with which that test will reject the null hypothesis in those situations in which it is false. This article focuses on the variables that influence power and discusses the different conceptions of power.A prioripower is computed before the experiment is conducted and is critical to selecting an appropriate sample size. Retrospective power is calculated on the basis of the completed study. This article outlines a number of concerns with retrospective power. Compromise power is based on the relative seriousness of Type I and Type II errors, and should be considered seriously for exploratory research. Finally, the article discusses the importance of considering confidence intervals on effect sizes, in addition to power.
Power Analysis for Categorical Methods
Authors: Erdfelder Edgar , Faul Franz , Buchner Axel 
Published: September 2005

Abstract

This article shows how to assess and control the power of chi‐square tests for categorical data. It discusses both the case of simple null hypotheses (i.e., the category or cell probabilities are completely specified
a priori
) and the case of composite null hypotheses for parameterized multinomial models requiring parameter estimation (e.g., log‐linear models). Power calculations are illustrated for chi‐square tests of association and chi‐square goodness‐of‐fit tests using standard examples from behavioral research.
Power and Sample Size in Multilevel Linear Models
Authors: Snijders Tom A. B. 
Published: September 2005

Abstract
Sample size determination in multilevel designs requires attention to the fact that statistical power depends on the total sample sizes for each level. It is usually desirable to have as many units as possible at the top level of the multilevel hierarchy. Some formulas are given to obtain insight in the design aspects that are most influential for standard errors and power.
Prediction Analysis of Cross‐Classifications
Authors: Szabat Kathryn A. 
Published: September 2005

Abstract

Prediction analysis is an approach to the analysis of cross‐classified, cross‐sectional or longitudinal data. It is a method for predicting events based on specification of predicted relations among qualitative variables and includes techniques for both stating and evaluating those event predictions. This article presents the basic methodology employed in prediction analysis and highlights approaches developed by Hildebrand, Laing, and Rosenthal (6), and by von Eye, Brandtstadter & Rovine, (12, 13). The Hildebrand et al. approach involves measuring prediction success of a set of predictions via a proportionate reduction in error (PRE) measure, known as
del
. The von Eye et al. approach models the prediction hypothesis via nonstandard log‐linear modeling and evaluates the extent to which the model adequately describes the data. Both approaches to prediction analysis provide a viable, valuable data analysis tool that researchers in the social and behavioral sciences can use in a wide range of research settings.
Prevalence
Authors: Grietens Hans 
Published: September 2005

AbstractPrevalence is determined as the proportion of individuals in a population who have a specified disease. Depending on the period of time that is taken into account, point, period, or lifetime prevalences can be calculated. Prevalence studies are part of epidemiological research. They are conducted in medicine, psychiatry, and behavioral science. They are useful to determine the needs for services in a certain area and allow the study of variations of outcomes over time and place. There is a dynamic relationship between prevalence and incidence. Prevalence rates are a function of both the incidence and the duration of a disease.
Principal Component Analysis
Authors: Jolliffe Ian 
Published: September 2005

Abstract

When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the
p
original variables by a smaller number,
q
, of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with
q
very much smaller than
p
. Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined.
Probits
Authors: Stefanescu Catalina , Berger Vance W. , Hershberger Scott L. 
Published: September 2005

AbstractThe Probit model is frequently used as a transformation of probabilities from an observed categorical variable's distribution into scale values. These scale values (‘probits’) are derived from the normally distributed, continuous latent variable that is assumed to underlie an observed categorical variable. Probit models are especially useful for modeling censored or truncated variables. Binary, ordinal, and multinominal categorical variables can be modeled by probit transformations.
Procrustes Analysis
Authors: Borg Ingwer 
Published: September 2005

Abstract
Procrustes analysis is concerned with fitting one or more configurations of points to some target configuration. In the simplest case, one configuration is rigidly rotated and/or reflected in space so that it matches a fixed target as closely as possible. A common application is to assess how closely the solution of a factor analysis for one sample corresponds to the solution for another sample or to a hypothesized pattern, respectively. The fitting problem can be generalized in several ways by admitting wider classes of transformations that may indicate empirically meaningful patterns of difference among the configurations.