bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compensatory Equalization
Authors: Onghena Patrick 
Published: September 2005

Abstract
Compensatory equalization is a threat to the treatment construct validity in intervention studies, in which comparison groups not obtaining the preferred treatment are provided with compensations that make the comparison groups more equal than originally planned. Compensatory equalization can seriously bias the estimation of treatment effects and should be avoided whenever possible, for example, by social isolation or masking of the comparison groups. If compensatory equalization cannot be avoided, then broadening the treatment effect construct seems the most viable option.
Compensatory Rivalry
Authors: Conrad Karen M. , Conrad Kendon J. 
Published: September 2005

Abstract
Compensatory rivalry is a potential threat to the construct validity of the cause in intervention studies. Compensatory rivalry exists when the study group not receiving the experimental treatment (i.e., intervention) feels disadvantaged, disappointed, or left out and decides to obtain a similar intervention on its own. It may or may not involve intentional competition or rivalry with the experimental group. While we are not likely to prevent compensatory rivalry from operating, we should be aware of its possibility, measure it, and if possible, statistically adjust for it. Minimally, we would want to consider its possibility in interpreting the study findings.
Completely Randomized Design
Authors: Maxwell Scott E. 
Published: September 2005

Abstract
The completely randomized designCompletely Randomized Design (CRD) is the simplest type of experimental design. Three characteristics define this design: (1) each individual is randomly assigned to a single treatment condition, (2) each individual has the same probability of being assigned to any specific treatment condition, and (3) each individual is independently assigned to treatment conditions.
Computer‐Based Test Designs
Authors: Zenisky April L. 
Published: September 2005

Abstract
Among the decisions to be made in implementing a computer‐based test, the choice of test design is one of the most important. A test design is the manner by which individual items are selected for presentation to examinees during administration. The importance of evaluating test design options is critical because it not only concerns test administration but also basic activities in the test development process and the quality and precision of scores to be obtained for a specified testing purpose. Essentially, conceptualizing the test design options for computerized testing first hinges on the property of being adaptive or not, and then there are varied approaches within each of those two groupings. The purpose of this entry is to provide an overview of selected test designs, adaptive and otherwise, while highlighting certain measurement advantages and concerns associated with each.
Software for Behavioral Genetics
Authors: Neale Michael C. 
Published: September 2005

Abstract
Algorithms for computing heritability and other statistics of interest to quantitative geneticists have been available for almost 100 years. In the past 20 years there has been considerable growth in software development for behavior genetic studies, which was largely motivated by a desire to obtain maximum likelihood estimates and to compare how well alternative models or hypotheses fit the data. Initially these methods were implemented in purpose‐built programs of very limited utility, which were difficult to use and to modify. LISREL overcame these problems by providing a more general modeling interface, but this was also found to be limited for the analysis of data collected from relatives. Mx was developed to reduce or eliminate these shortcomings; having a matrix algebra interpreter it permits specification of complex models for both traditional genetic epidemiology such as twin studies, as well as for linkage and association studies, and permits specification of mixture distributions and alternate model fitting methods.
Computer‐Adaptive Testing
Authors: Luecht Richard M. 
Published: September 2005

AbstractThis entry presents an overview of computer‐adaptive testing (CAT). The basic CAT algorithm is introduced, followed by a discussion of item response theory (IRT) test information functions and their role in adaptive testing. Issues such as test efficiency and security risks under CAT are also discussed. The entry concludes with a description of four promising CAT variants, including CAT shadow tests, a‐stratified CAT, testlet‐based CAT, and computer‐adaptive multistage testing.
Confidence Intervals
Authors: Dracup Chris 
Published: September 2005

AbstractIn estimating a population parameter from sample data, it is more informative to provide a range of ‘plausible’ parameter values rather than a one‐point estimate. A 95% confidence interval contains those values of the population parameter that would not be rejected at the 0.05 significance level by a statistical test applied to the sample data. In the long run, such intervals should provide 95% ‘coverage,’ with 95% of them including their true population value (assuming the data meet any distribution assumptions). Topics covered include the construction of confidence intervals for means, proportions, and mean differences; the effects of discrete and nonsymmetrical sampling distributions; central and noncentral intervals; bootstrapped intervals and the Bayesian approach.
Confidence Intervals: Nonparametric
Authors: Garthwaite Paul H. 
Published: September 2005

Abstract

These procedures for forming confidence intervals are attractive because they require weaker assumptions than parametric methods. Several of the procedures exploit the relationship between hypothesis tests and confidence intervals and these require hypotheses to be found that give specified
P
values. Finding the appropriate hypotheses can be computationally demanding, but for some problems there are efficient search techniques.
Configural Frequency Analysis
Authors: von Eye Alexander 
Published: September 2005

AbstractConfigural Frequency Analysis (CFA) is a method for the analysis of cross‐classifications of categorical variables. Using CFA, one examines individual cells or groups of cells of cross‐classifications with the goal of determining whether they contain more or fewer cases than expected based on some chance model. The specification of the chance model determines the interpretation of results. Depending on chance model, cells with more or fewer cases than expected can be interpreted as indicative of local associations, but also of predictor‐criterion relationships; they allow one to distinguish between groups, or they reflect typical and atypical patterns of change over time.
Factor Analysis: Confirmatory
Authors: Byrne Barbara M. 
Published: September 2005

Abstract
The intent of this article is to present an introductory overview of the basic concepts and testing procedures associated with confirmatory factor analysis (CFA). Working from a hypothesized four‐factor structure of a nonacademic self‐concept (SC), as measured by 32 items from the Self Description Questionnaire, readers are introduced to both a graphical and a structural equation specification of this CFA model, followed by a review of the related procedures involved in its testing, estimation, and evaluation, together with the interpretation of results. Important issues and caveats associated with the testing of CFA models are also briefly addressed.
Confounding in the Analysis of Variance
Authors: Bogartz Richard S. 
Published: September 2005

Abstract
Unintentional and intentional confounding in the analysis of variance is discussed. Some different kinds of unintentional confounding that can occur in between‐subject designs, within‐subject designs, and mixed designs are considered. The usefulness of losing information to conserve resources by intentionally confounding variables gives rise to split‐plot, confounded factorial, and fractional replication designs. An example from infancy research is provided.
Confounding Variable
Authors: Onghena Patrick , van den Noortgate Wim 
Published: September 2005

Abstract
A confounding variable is a variable that is related to an explanatory variable and at the same time has an effect on a response variable that is entangled with a possible effect of the explanatory variable, possibly leading to wrong conclusions about the latter. Confounding may also occur in situations involving more than one explanatory or response variable, and more than one confounding variable may be present. Observational studies, quasi‐experiments, as well as randomized experiments might be affected by confounding. If confounding variables cannot be avoided or ruled out by design, a final option is to model them as covariates.
Contingency Tables
Authors: Everitt Brian S. 
Published: September 2005

AbstractContingency table arise from cross‐classifying a sample of observation with respect to a number of categorical variables. Each cell of such a table contains a count of the observation falling in the cell. In the most common type of contingency table arising from two categorical variables, interest generally centers on whether the two variables are independent. The relevant test statistic has, for large sample size, a chi‐squared distribution. Tables involving more than two variables are usually analyzed with log‐linear models.
Correlation Issues in Genetics Research
Authors: Cherny Stacey S. 
Published: September 2005

Abstract

Correlation is of central importance in behavioral genetic research. A basic goal of behavioral genetic research is to partition variance in a trait into variance due to genetic differences among people (commonly referred to as
heritability
(or
h
2
), variance because of environmental influences shared among family members (shared environmental variance, or
c
2
), and nonshared environmental variance as a result of unique individual experiences (
e
2
) (
see
ACE Model
). This is accomplished by analyzing correlational structure within different familial relationships. Although in practice we perform
maximum‐likelihood
model‐fitting analyses on raw data and partition (unstandardized) covariance structure, examining familial correlations directly is the best way of quickly getting an idea of what is going on in the data.
Co‐twin Control Methods
Authors: Goldberg Jack , Fischer Mary 
Published: September 2005

AbstractThe co‐twin control method is an elegant research design that uses differences within twin pairs to examine the association between a putative environmental risk factor and an outcome variable. The design is extremely efficient for examining risk factor–outcome associations compared to unmatched designs. This is especially true when using monozygotic (MZ) twins who share 100% of their genetic material. Co‐twin control studies can be used in both the experimental and observational settings. Statistical analysis for continuously distributed outcome variables focuses on the within‐pair mean difference, while for dichotomous outcomes, the matched pair odds ratio is used. Whenever possible, it is informative to examine differences in the relationship between the risk factor–outcome association by zygosity; findings that are only present in dizygotic (DZ) pairs suggest that the observed association may be due to genetic influence on both the risk factor and outcome. We present an illustration of the analysis of a co‐twin control study, looking at the association between military service in Vietnam and posttraumatic stress disorder, with data from the Vietnam Era Twin Registry. A wide range of new regression modeling methods, which account for nonindependent observations, can be readily adapted for co‐twin control studies.
Counternull Value of an Effect Size
Authors: Rosenthal Robert 
Published: September 2005

Abstract

The counternull value of an effect size is the nonnull magnitude of effect size that is supported by exactly the same amount of evidence as supports the null value of the effect size. That is, if the counternull value were taken as the null hypothesis, the resulting
P
value would be the same as the obtained
P
value for the actual null hypothesis. Reporting the counternull, in addition to the
P
value, virtually eliminates the common error of equating failure to reject the null with the estimation of the effect size as equal to zero.
Counterbalancing
Authors: DePuy Venita , Berger Vance W. 
Published: September 2005

AbstractWhen subjects are consecutively given more than one treatment in an experiment, including for crossover design, earlier treatments may affect the results observed during later treatments. To counteract this, the treatment order may be varied between different subjects, or counterbalanced. While complete counterbalancing often requires large numbers of participants, partial counterbalancing, often through the use of a Latin squares design, can provide a reasonable alternative.
Counterfactual Reasoning
Authors: Holland Paul W. 
Published: September 2005

AbstractCounterfactual conditional statements underlie definitions of causation. The substitution of factual, observed data in control groups for counterfactual information about treatment groups is the basis of causal inference in randomized experiments and observational studies. Formal models that give causal interpretations to statistical calculations and that involve counterfactual quantities were introduced as early as 1923.
Analysis of Covariance: Nonparametric
Authors: Berger Vance W. 
Published: September 2005

Abstract
There are numerous benefits in adjusting an analysis for one or several covariates, but often these benefits come at the price of having to make additional assumptions, such as distributional assumptions or assumptions regarding the relationship between a covariate and a response variable. For example, the validity of the popular analysis of covariance model is predicated on a variety of assumptions. Clearly, the need to make assumptions, or to restrict the results so that they apply only when these assumptions are true, is a weakness, and it would be preferable to reap the benefits of covariate adjustment without having to rely on unreasonable assumptions. There is a body of nonparametric covariate adjustment techniques that aim to allow for this. Specific techniques depend on the nature of the covariate (e.g., binary or continuous).
Covariance Structure Models
Authors: Thum Y. M. 
Published: September 2005

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.
Covariance/Variance/Correlation
Authors: Rodgers Joseph Lee 
Published: September 2005

Abstract
In this entry, I describe the covariance, variance, and correlation, each in a common framework that allows their interrelationships to be apparent. Specifically, the covariance is the uniting concept, within which the variance and correlation are special cases. Following separate treatments, I present a formula that explicitly shows the relationships between the three indices. Finally, I briefly describe the role of each in Structural Equations Modeling (SEM).
Calculating Covariance
Authors: Chen Gilad 
Published: September 2005

Abstract

Covariance is a measure of association, or covariability, between two variables (e.g., variables
X
and
Y
). A positive covariance indicates positive association between
X
and
Y
, whereas a negative covariance indicates a negative association between
X
and
Y
. However, the magnitude of covariance is heavily dependent on the unit of measurement used to measure
X
and
Y
Criterion‐Referenced Assessment
Authors: Hambleton Ronald K. , Li Shuhong 
Published: September 2005

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.
Cross‐Lagged Panel Design
Authors: Kenny David A. 
Published: September 2005

Abstract
The minimal cross‐lagged panel design is two variables measured at two times. Multiple regression analyses, in which the time two measures are regressed on the time one measures, have become the standard method of analyzing the design. Currently, methods to rule out third‐variable causation are little used. Several analysts have argued that two waves are insufficient to obtain much information about how processes unfold over time.
Demand Characteristics
Authors: de Munter Agnes 
Published: September 2005

AbstractDemand characteristics come into being when research data are collected through exchange of information between human participants. It is this complex of unintended or unverifiable indications in the research situation that make the respondent believe he or she can guess the researcher's hypotheses or objectives. They are systematic errors, which form a threat to validity. In research, they can be traced by means of ‘quasi‐control’ strategies. It is hoped that this form of bias can be neutralized by restricting the interaction between researcher and respondent.
Direct and Indirect Effects
Authors: Muller Dominique , Judd Charles M. 
Published: September 2005

Abstract

A three‐variable model with one independent variable (

X

), one dependent variable (

Y

), and a possible mediator (or suppressor) can be decomposed into three different types of effects. The first one is the indirect effect which is the portion of the relationship between

X

and

Y

that goes through the mediator. The second one is the direct effect which is the portion of the relationship between

X

and

Y

that does not go through the mediator. And, finally, the total effect is the sum of both the direct and the indirect effects.
Dominance
Authors: Evans David M. 
Published: September 2005

Abstract

Dominance refers to the nonlinear interaction between alleles at the same locus. In the case of a discrete trait, if one allele masks the effect of the other allele at the same locus, we say that the first allele displays dominance over the second allele. In the case of a quantitative trait, the amount of dominance at a locus may be quantified by the dominance parameter
d
, which equals the deviation of the heterozygote's phenotypic value from the midpoint of the two homozygotes' phenotypic values. In the case of a single biallelic locus in Hardy–Weinberg equilibrium, the dominance variance is given by the formula (2
pqd
)
2
where
p
and
q
are the allele frequencies at the locus. The amount of dominance variance affecting a trait can be estimated using the classical twin design.
Dropouts in Longitudinal Data
Authors: de Leeuw Edith D. 
Published: September 2005

Abstract
Different types of nonresponse threaten the validity of longitudinal studies: First, the initial nonresponse during the recruitment in the base‐line survey; Second, successive dropout at each time point. In this entry, specific attention is given to dropout and strategies to limit the problem. Dropout in longitudinal surveys has three separate sources: failure to locate research participants, failure to contact research participants, and failure to achieve cooperation. Effective measures are described to reduce nonresponse. To limit nonresponse, a total design approach is advocated with specific attention to each source. That is to limit both noncontact (i.e., failure to locate and subsequent failure to contact a located research participant), and noncooperation.
Dropouts in Longitudinal Studies: Methods of Analysis
Authors: Little Roderick J. 
Published: September 2005

Abstract
The statistical analysis of repeated‐measures data when some participants drop out prior to the end of the study has been reviewed. Methods considered include complete‐case analysis, where only the cases that are followed to the end of the study, are analyzed; imputation methods that fill in values after drop out, and multiple imputation, a refinement that accounts for imputation uncertainty; and methods that can be applied to the nonrectangular data set that arises from the missing data, including maximum likelihood for linear and nonlinear mixed models. Methods of analysis when the missing data are not missing at random are also reviewed.
Educational Psychology: Measuring Change Over Time
Authors: Kulikowich Jonna M. 
Published: September 2005

Abstract
Educational psychologists use repeated measures analyses to study changes over time that reflect academic, cognitive, emotional, and social development of students. The purpose of this chapter is to review the basic principles and procedures of repeated measures in statistics, and, based on these principles and procedures, to describe important considerations of educational psychologists as they attempt to answer research questions. Testing of assumptions, estimating effect sizes and power, and current statistical advances in the study of how individuals' affect, behavior, and cognition change are discussed.
Effect Size Measures
Authors: Kirk Roger E. 
Published: September 2005

AbstractA variety of measures of effect magnitude are described. Many of the measures can be classified as either measures of effect size (typically, standardized mean differences) or measures of strength of association. There also is a large group of statistics that do not fit into either category. Measures of effect magnitude are used for a variety of purposes: (a) to estimate the sample size required to achieve an acceptable power, (b) to integrate the results of empirical research studies in meta‐analyses, (c) to supplement the information provided by null hypothesis significance tests, and (d) to determine whether research results are practically significant.
Epistasis
Authors: Evans David M. 
Published: September 2005

Abstract
Biologically speaking, epistasis refers to the interaction between genes at different loci to produce effects which are different from that expected from the individual loci themselves. In quantitative genetic terms, epistasis refers to a deviation from additivity in the effect of alleles and genotypes at different loci with respect to their contribution to a quantitative phenotype. The proportion of variance due to epistasis may be estimated using careful breeding experiments in experimental organisms, but cannot be estimated using the classical twin design. It is possible to model epistasis explicitly in linkage and association analyses.
Error Rates
Authors: Thompson Bruce 
Published: September 2005

Abstract

Error rate refers to the probability of making one or more incorrect decisions regarding null hypotheses. The most commonly recognized forms of errors are Type I errors (i.e., rejecting a null that actually is true) and Type II errors (i.e., failing to reject a null that actually is false). Researchers also distinguish between making a Type I error on a given hypothesis test (called a
testwise Type I error
) and making one or more Type I errors in a set of hypothesis tests (called
experimentwise Type I error
).