bluuebunny/crossref_metadata_2025 huggingface dataset:
Winsorized Robust Measures
Authors: Wilcox Rand R.
Published: September 2014

Winsorizing observations leads to robust measures of location, variation, and correlation, and it plays a fundamental role when making inferences about trimmed means.
Binomial Test
Authors: Wagner‐Menghin Michaela M.
Published: September 2014

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.
N
eyman–
P
earson Inference
Authors: Nickerson Raymond S.
Published: September 2014

The approach to statistical hypothesis testing developed by Jerzy Neyman and Egon Pearson built upon and extended that of Ronald Fisher, and differed from the latter in several important ways. Ideas that distinguish their approach include choosing between possible hypotheses (in contrast to deciding whether or not to reject a single hypothesis); setting, before experimentation, a criterion for deciding which hypothesis to accept; recognizing the two possible ways to draw an incorrect conclusion from a statistical test and taking account of the fact that they are not likely to be equally desirable; and defining the power of a test as the probability of detecting a real effect if there is one. They saw statistical tests as attempts to quantify commonsense notions about the interpretation of probabilistic data and stressed the importance of the exercise of judgment in their application.
Classical Statistical Inference: Practice Versus Presentation
Authors: Harris Richard J.
Published: September 2014

Classical statistical inference is a useful tool in determining whether sufficient evidence has been marshaled to establish the direction of a population effect. Ifp(the probability that a sample effect as large as or larger than the observed effect size would be obtained when sampling from a population in which the effect size is zero) is very low, we conclude that the population effect goes in the same direction as the sample effect. Otherwise, we conclude that there is insufficient evidence to be confident of the direction of the effect in the population. Misunderstanding and misrepresentation of this logic has a number of unfortunate consequences against which I attempt to inoculate the reader.
Robustness of Standard Tests
Authors: Wilcox Rand R.
Published: September 2014

Conventional hypothesis‐testing methods such as student'st, the ANOVAFtest, and methods based on the ordinary least squares regression estimator, are not robust to violations of assumptions. In fact, there are general conditions under which these methods can provide poor control over the probability of a type I error and inaccurate confidence intervals, no matter how large the sample sizes might be. Relatively poor power is yet another concern.
Multiple Comparison Procedures
Authors: Keselman H. J. , Holland Burt , Cribbie Robert A.
Published: September 2014

Multiple comparison procedures (MCPs) are frequently adopted by applied researchers to locate specific differences between treatment groups. That is, omnibus test statistics, such as the analysis of varianceFtest, can only signify that effects are present, not which specific groups differ from one another (when there are more than two groups). In our paper, we discuss MCPs that can be used to investigate simple pairwise differences between treatment group means, as well as MCPs that can be used to examine complex comparisons (i.e., nonpairwise comparisons) between treatment group means. In particular, we discuss simultaneous as well as stepwise MCPs, emphasizing procedures that can be utilized when the derivational assumptions of the classical procedures of normality and variance homogeneity do not hold.
Sensitivity Analysis: Overview
Authors: Wakefield Jon
Published: September 2014

In an observational study, there is always the possibility of bias due to unmeasured variables, measurement error in explanatory variables and/or selection bias; and randomized studies are also not immune to at least some of these biases. A sensitivity analysis provides a means of asking a series of ‘what if?’ questions, in order to determine the robustness of the observed association to sources of bias.
Multiple Testing
Authors: Darlington Richard B.
Published: September 2014

We examine methods designed to correct for the fact that when many significance tests are performed, some are likely to be significant just by chance.
Bradley–Terry Model
Authors: Marden John I.
Published: September 2014

In a paired comparison experiment, several judges compare several objects. Each judge performs a number of paired comparisons, that is, considers a pair of the objects, and indicates which of the two is preferred. The Bradley–Terry model is a simple but powerful model in which each object is equipped with a measure of overall strength, and the chance that one object is preferred to another depends on only their relative strengths. The model is useful not only for preference data but also for competitions, for example, among sports teams.
Measurement: Overview
Authors: Michell Joel
Published: September 2014

Measurement in behavioral science includes practices over and above those used in physical and biological science. In part because of this, the understanding of measurement within behavioral science is different from the
traditional concept of measurement
. This latter concept is built upon that of a quantitative attribute (i.e., one possessing internal additive structure and sustaining ratios) and it defines measurement as estimation of the ratio between a magnitude of a quantitative attribute and a unit. However, it is not clear whether the attributes specific to behavioral science are quantitative. Consequently, some behavioral scientists favor a
representational concept of measurement
, according to which measurement depends upon isomorphisms or homomorphisms between empirical and numerical systems. This understanding extends the concept of measurement to ordinal and nominal scaling. However, within the framework of the representational concept, the status of one major tradition of quantification in behavioral science, namely psychometrics, is unclear. This has led many behavioral scientists to endorse the
operational concept of measurement
, according to which measurement is any precisely specified operation that yields a number. Most behavioral scientists agree that measurement is the assignment of numerals to objects and events according to rules, a definition open to representational and operational interpretations.
Functional data analysis ‐ An Introduction
Authors: Ramsay James
Published: September 2014

Functional data analysis (FDA) models data using functions or functional parameters. The complexity of the functions is not assumed to be known in advance, so that methods are used for approximating these with as much flexibility as the data require.
Multidimensional Item Response Theory Models
Authors: Ackerman Terry A.
Published: September 2014

It is common for educational and psychological tests to be inherently multidimensional, or measuring two or more traits, skills, or dimensions. If assessment data are determined to be multidimensional testing practitioners, researchers can apply multidimensional item response theory (MIRT) to help them better understand what their tests are measuring, how accurately the different composites of ability are being assessed, and use this information to inform the test development process. In this entry, several different aspects of MIRT analyses are discussed: obtaining evidence that the test is multidimensional, estimating the item and ability parameters for a multidimensional model, and illustrating the estimated parameters graphically to gain a better understanding about the composites of traits or skills that are being assessed.
Validity Theory and Applications
Authors: Sireci Stephen G.
Published: September 2014

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.
Markov ChainMonteCarlo Item Response Theory Estimation
Authors: Keller Lisa A.
Published: September 2014

Item response theory (IRT) is the cornerstone of modern measurement. The benefits of using IRT have been well documented. In order to use IRT models, it is necessary to estimate item parameters. As assessments become more complex, more sophisticated models need to be developed. The difficulty in making models more complex is the issue of parameter estimation, and, without the ability to estimate parameters, the models are not useful. With the growth of computer technology, new estimation techniques are being developed for IRT. One such technique is Markov Chain Monte Carlo (MCMC). MCMC provides parameter estimates by simulating draws from the posterior distribution of the parameters, and uses the constructed samples to provide point estimates of the parameters (e.g., means, modes). Both Gibbs sampling and Metropolis–Hastings within Gibbs are described, and examples of estimating parameters of dichotomous IRT models are provided.
Reliability: Definitions and Estimation
Authors: Mead Alan D.
Published: September 2014

Reliability, ρ

XX
′

, is the degree to which test scores are precise and free from error. Classical test theory defines reliability as the ratio of true score variance to observed score variance: . This article provides a comprehensive definition of reliability and then describes the methods by which reliability is commonly estimated, including their limitations. The article then discusses several closely‐related topics: the standard error of measurement; reliability in subpopulations; the Spearman‐Brown prophesy formula; true score correlations; reliability of speeded tests; and the relationship between classical reliability and item response theory information.
New Item Types and Scoring
Authors: Drasgow Fritz , Mattern Krista
Published: September 2014

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.
Heuristics: Fast and Frugal
Authors: Hoffrage Ulrich
Published: September 2014

Fast and frugal heuristics can be used to make cue‐based inferences about an uncertain world. They are models of bounded rationality in the sense that they continue to yield a good performance even under conditions of limited time, limited knowledge, and limited computational complexity. They are specified in terms of search rules, stopping rules, and decision rules. They are robust and can outperform more complex strategies, because and not in spite of their simplicity.
Model Evaluation
Authors: Navarro Daniel J. , Myung Jay I.
Published: September 2014

Assessing the viability of a quantitative model given observed data is generally done in three steps: model fitting, testing, and selection. In model fitting, we find the parameter values that best fit observed data. This is followed by model testing in which null hypothesis significance tests allow us to evaluate the descriptive adequacy of a model's fit to data. Model selection concerns the issue of choosing the model, among a set of competing models, that generalizes best to as yet unseen data samples.
Proximity Measures
Authors: Bock Hans‐Hermann
Published: September 2014

Proximities describe the similarity or dissimilarity between items or objects in a numerical way. General properties and special definitions are presented. Proximities are typically used for analyzing and visualizing hidden similarity structures by graphical displays or when looking for homogeneous clusters in a set of data points. Various types of dissimilarity measures are uniquely related to special types of clusterings or visualizations. Finally, we characterize the proximity between probability distributions by various divergence measures.
Multivariate Outliers
Authors: Fieller Nick
Published: September 2014

Outliers in multivariate data provide particular problems primarily because they may not be revealed as an extreme observation in any single component and are, consequently, easy to miss in routine graphical inspection of the data. This article describes some graphical techniques particularly directed towards the objective of detecting outliers before considering more formal methods of testing for discordancy and overcoming the problems of masking and swamping.
Correlation Studies
Authors: Busk Patricia L.
Published: September 2014

Correlational study is a specific type of descriptive research in which the researcher is interested in relationships between two or more variables. Researchers collect data for these variables on a single sample of adequate size.
Multidimensional Scaling
Authors: F. Groenen Patrick J. , Velden Michel
Published: September 2014

Multidimensional scaling is a statistical technique to visualize dissimilarity data. In multidimensional scaling, objects are represented as points in a usually two dimensional space, such that the distances between the points match the observed dissimilarities as closely as possible. Here, we discuss what kind of data can be used for multidimensional scaling, what the essence of the technique is, how to choose the dimensionality, transformations of the dissimilarities, and some pitfalls to watch out for when using multidimensional scaling.
Structural Equation Modeling: Mixture Models
Authors: Vermunt Jeroen K. , Magidson Jay
Published: September 2014

This article discusses a modeling framework that links two well‐known statistical methods: structural equation modeling (SEM) and latent class or finite mixture modeling. Mixture SEM can either be viewed as a refinement of multivariate normal (MVN) mixtures, where the within‐class covariance matrices are smoothed according to a postulated SEM structure, or as a form of multiple group SEM, in which the group variable is unobserved. After introducing standard MVN mixtures, it is shown how the SEM framework can be used to restrict the class‐specific means and covariances. Furthermore, parameter estimation, model testing, and software is discussed, and an empirical example is provided.
Factor Analysis: Exploratory
Authors: Pruzek Robert
Published: September 2014

Exploratory factor analysis (EFA) provides a way to account for intercorrelations or covariances among all pairs of variables in a battery using a relatively small number of latent variables called common factors. The method can be especially helpful for analyzing structural relationships in situations where observed variables are approximately linearly related to one another, and when individual‐level observations are fallible, that is, subject to errors of measurement. Exploratory factor analysis has gained prominence in many fields over many years, and it has been the subject of much praise as well as criticism. Psychometric and statistical practice has not fully caught up with contemporary appreciations of factor analytic and related methods, but past and recent developments suggest notable promise for continued applications in applied sciences.
Scatterplot Matrices
Authors: Wright Daniel B. , Williams Siân E.
Published: September 2014

A scatterplot matrix is a display for viewing the bivariate relationships among several pairs of variables. Variations, like trellis plots, show bivariate relationships at a different level of a third variable.
Partial Correlation Coefficients
Authors: Brown Bruce L. , Hendrix Suzanne B.
Published: September 2014

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.
Horseshoe Pattern
Authors: Everitt Brian S.
Published: September 2014

When applying multidimensional scaling to data that has an inherent one‐dimensional structure, for example, in a chronological study, the two‐dimensional scaling solution may often arrange the points in a ‘horseshoe’ shape, which is a consequence of the ‘blurring’ of the larger distances.
Reproduced Matrix
Authors: Lance Charles E.
Published: September 2014

A reproduced matrix is a matrix of correlations or covariances that is calculated from parameter estimats obtained for a path analytic, factor analytic, or latent variable structural equation model. If one or more ‘zero restrictions’ are specified, model goodness‐of‐fit can be evaluated by the extent to whihc the reproduced matrix equals (or approximates) the observed matrix of correlations or covariances calculated directly from the data, for exdample, using the chi‐square statistic.
Scaling Asymmetric Matrices
Authors: Takane Yoshio
Published: September 2014

Square tables in which some of the elements located symmetrically about the diagonal are not equal to each other are called asymmetric tables. Asymmetric tables arise in a variety of different ways. They can be roughly classified into two groups, one arising from measurements of antisymmetric relationships, and the other, of proximity relationships. A variety of models have been proposed to capture the asymmetry in such tables. In this article, we briefly discuss some of the representative models of asymmetry for each of the two types of data.
Residuals in Structural Equation, Factor Analysis, and Path Analysis Models
Authors: Raykov Tenko
Published: September 2014

Residuals are defined for structural equation, factor analysis, and path analysis models. Their defining equations are given and it is pointed out how they can be obtained in empirical contexts. The different types of residuals are particularly useful when examining local goodness of fit for those models.
Structural Equation Modeling: Multilevel
Authors: Steele Fiona
Published: September 2014

Factor analysis and structural equation modelling of clustered data are discussed. The application of these methods, with emphasis on the interpretation of results, is illustrated in analyses of students' scores on a set of science tests. These data have a two‐level hierarchical structure with students nested within schools.
Boosting
Authors: Cutler Adele
Published: September 2014

Boosting is a method that attempts to increase the prediction accuracy of a classification or regression method by repeatedly re‐weighting the data and combining the results. The boosted classifier generally had good accuracy, but it is not readily interpretable.
Carroll–Arabie Taxonomy
Authors: Leeuw Jan
Published: September 2014

The Carroll–Arabie taxonomy of scaling methods gives a way to systematically describe data and models used in the computerized geometrical approach to scaling developed in psychometrics since 1960.
Latent Variable
Authors: Vermunt Jeroen K. , Magidson Jay
Published: September 2014

Latent variable techniques are used as scaling tools when multiple responses related to the same construct are available. The differences between the four main types of latent variable models – factor analysis, latent trait analysis, latent profile analysis, and latent class analysis are described.
Bagging
Authors: Cutler Adele , Corcoran Chris , Toone Leslie
Published: September 2014

Bagging stands for ‘
b
ootstrap
ag
gregating’ and means that we apply a regression or classification procedure to bootstrap samples from the original data and aggregate the results. A ‘bagged’ procedure is often much more accurate than the original, although it is slower and more difficult to interpret.
Factor Analysis: Confirmatory
Authors: Byrne Barbara M.
Published: September 2014

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.
Analysis of Covariance: Nonparametric
Authors: Berger Vance W.
Published: September 2014

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).
K
endall's Coefficient of Concordance
Authors: Field Andy P.
Published: September 2014

Kendall's Coefficient of Concordance,
W
, is a measure of the agreement between several judges who have rank ordered several entities. It represents the ratio of the variability of the total ranks for the ranked entities to the maximum possible variability of the total ranks; a small ratio implies disagreement between judges.
S
pearman's Rho
Authors: Kornbrot Diana
Published: September 2014

Spearman's rho,
r

s

, is a correlation measure of association based on ranks of scores rather than raw scores. It is applicable if either or both variables are direct rankings or only ordinal. It is also applicable when both variables are metric but not normally distributed, or when the relation between the variables is monotonic but nonlinear.
Wilcoxon‐Mann‐Whitney Test: Overview
Authors: DePuy Venita , Berger Vance W. , Zhou YanYan
Published: September 2014

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.
Polychoric Correlation
Authors: Hershberger Scott L.
Published: September 2014

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.
Cramer‐Von Mises Test
Authors: Lunneborg Clifford E.
Published: September 2014

The Cramér–von Mises test assesses whether two independent samples could have been drawn from the same or equivalent distributions.
Signed Ranks Test
Authors: Sawilowsky Shlomo , Fahoome Gail
Published: September 2014

The Signed Rank test is a nonparametric alternative to the one‐sample or paired samples parametric
t
Test.
Paired Observations, Distribution Free Methods
Authors: Berger Vance W. , Li Zhen
Published: September 2014

Data 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.
Randomized Block Design: Nonparametric Analyses
Authors: Liu Li , Berger Vance W.
Published: September 2014

In a randomized block design, there are, in addition to the experimental factor or factors of interest, one or more nuisance factors. The role of blocking is to reduce or eliminate that part of the experimental error attributable to these nuisance factors. Standard parametric ANOVA can be used to analyze such designs, but it requires the normality assumption, and may be misleading if the errors are not normally distributed or if there are outliers. In this article, we consider some distribution‐free tests, such as the sign test, the Wilcoxon signed rank test, Friedman's test, aligned rank tests, Durbin's test, and the row mean score test. None of these methods require the normality assumption, and they can all be used to analyze such designs.
One Way Designs: Nonparametric and Resampling Approaches
Authors: Hayes Andrew F.
Published: September 2014

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.
Generalized Linear Models: Introduction
Authors: Everitt Brian S.
Published: September 2014

Generalized linear models provide a unified framework for regression models including multiple regression, logistic regression, analysis of variance, and analysis of covariance. Such models consist of three components – a link function specifying the transformation of the response variable to be modeled by a linear function of the explanatory variables, an error distribution suitable for each type of response, and a variance function specifying the relationship between the mean and variance of the error distribution.
Ordinal Regression Models
Authors: Hershberger Scott L.
Published: September 2014

Regression models for ordinal data have been developed based upon the cumulative probabilities for the categories of response variable. One of the most frequently used ordinal regression model is the ordinal logistic model, a member of the family of generalized linear models.
Analysis of Variance: Multiple Regression Approaches
Authors: Harris Richard J.
Published: September 2014

One‐way and factorial independent‐means analysis of variance (ANOVA) can be accomplished via a multiple regression analysis (MRA) in which the dependent variable is predicted from sets of level‐membership variables or, preferably, contrast variables representing the factors. Doing so can be useful in the two‐group design in that it provides an easily interpretable and often remarkably low estimate of the proportion of variance in the dependent variable, that is accounted for by an experimental manipulation. It also helps in the choice among alternative approaches to analyzing unequal‐
n
factorial designs by showing that full‐model analyses involve the comparison of unweighted means and that testing an effect uncorrected for any other effects implies making comparisons among weighted means. Furthermore, testing an effect corrected for confounds with some, but not all, other effects involves comparisons among the means that are difficult to characterize, require hand computations beyond those provided by ANOVA computer programs, and are unlikely to be of any real interest to the researcher. SPSS commands for carrying out factorial ANOVA via the MANOVA and REGRESSION commands are provided.
Nonlinear Mixed Effects Models
Authors: Lindstrom Mary J. , Vorperian Houri K.
Published: September 2014

Nonlinear mixed‐effects (NLME) models are a generalization of LME and nonlinear regression models and are used when data have a hierarchical growth‐curve structure where an LME model might be used but a nonlinear model is required to fit the data. Both LME and NLME models require some care in estimating and testing model parameters. NLME models have additional difficulties due to the lack of a closed form for the marginal distribution of the data (see following sections).
Intraclass Correlation
Authors: Field Andy P.
Published: September 2014

Intraclass correlations are used to assess the consistency between measures of the same class (i.e., measures of the same thing). Two common uses are in comparing paired data (such as twins) on the same measure and assessing the consistency between judges' ratings of a set of objects. There are different conceptualizations of the intraclass correlation and the variance components used to calculate them in these different models. There is an important distinction between the cases when the measures are a single score or an average of multiple scores, and when a measure of consistency or a measure of absolute agreement is required.
Mallows' Cp Statistic I
Authors: Lance Charles E.
Published: September 2014

Mallows Cp statistic is used in multiple regression analysis to select models that contain smaller numbers of predictors from a larger number that is available for inclusion. Low values of Cp indicate regression models that are parsimonious yet still have good predictive ability.
Regression Model Coding for the Analysis of Variance
Authors: Cohen Patricia
Published: September 2014

Analyses that are typically carried out by analysis of variance (ANOVA) can often be carried out with multiple regression (MR) methods as well. The MR approach can include direct tests of the study's specific hypotheses and cope efficiently with unbalanced designs. The methods of coding groups for MR analysis should depend on the study design and hypotheses. Alternative methods are illustrated and discussed.
Moderation
Authors: Beaubien J. Matthew
Published: September 2014

Moderator variables, also known as
interaction effects
, alter the strength or direction of a predictor–criterion correlation. The identification of important moderator variables helps social scientists to interpret conflicting research results and, by extension, build more accurate theories of complex human behavior.
Regression Models
Authors: Lewis‐Beck Michael S.
Published: September 2014

Regression analysis is the most widely employed statistical technique in the study of observational data in the behavioral sciences. A dependent variable is posited as a function of an independent variable(s). The most common procedure for estimating a regression equation is ordinary least squares, although there are others.
Design Effects
Authors: Klar Neil , Donner Allan
Published: September 2014

The purpose of this article is to review the concept of design effect as originally defined in the context of survey sampling, but also to demonstrate its strong relevance to broader areas of scientific interest. A worked example is provided illustrating its application of this concept to the planning of sample size requirements for a school‐based cluster randomization trial.
Population Stratification
Authors: Oord Edwin J. C. G.
Published: September 2014

In case‐control studies, population stratification can cause associations between marker alleles and disease status. These associations are ‘spurious’ because the marker is not informative about the location of a disease mutation. Family‐based tests are an important tool to avoid such spurious findings but the extent to which these tests are really needed is a topic of debate.
Survey Sampling Procedures
Authors: Moore Kris K.
Published: September 2014

Survey sampling provides procedures to estimate characteristics of a population based on a sample. Questionnaire design, collection of sample information, sampling methods, and sample size are discussed.
Dropouts in Longitudinal Data
Authors: Leeuw Edith D.
Published: September 2014

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.
Attrition
Authors: Shadish William R. , Luellen Jason K.
Published: September 2014

Attrition refers to a loss of response from study participants. Random attrition reduces statistical power but does not bias the results of a study. Systematic attrition reduces statistical power and can lead to generalizability biases and causal inference biases. For these reasons, it is best to prevent or minimize attrition. Attrition from measurement can and should be avoided even when attrition from treatment cannot be avoided. For attrition that cannot be prevented, a number of alternative strategies attempt to account for attrition during data analysis.
Randomization: Overview
Authors: Boik Robert J.
Published: September 2014

Behavioral scientists were among the earliest to use randomization in research. Justifications for using randomization are plentiful and include the following: it ensures that estimators are unbiased and consistent; it ensures that permutation tests and associated confidence intervals are valid; it justifies normal‐theory tests, even in the absence of normality; it protects against subjective biases of the investigator; it helps to elucidate causality; and it enhances the robustness of inferences. Nonetheless, randomization is not universally accepted. Randomization has been criticized because it violates the conditionality and likelihood principles and because it can be less efficient than methods based on optimal sampling and optimal design.
Sequential Testing
Authors: Coad D. S.
Published: September 2014

The use of sequential methods in clinical trials dates back to the 1940s. With these methods, the accruing data are inspected periodically in order to assess whether the trial should be continued. Trials can be stopped when there is convincing evidence of a treatment effect or they can be stopped for futility. The use of sequential methods can reduce the numbers of patients in the trial compared with a design with a fixed sample size.
Placebo Effect
Authors: Onghena Patrick
Published: September 2014

A placebo effect is conceptually defined as the beneficial effect associated with an intervention that does not include the presumed active ingredients. It can be operationally defined as the positive difference between the results of a placebo condition and a no‐treatment condition in a randomized trial. Placebos play a crucial role as a control in clinical medication trials, but discussion remains regarding their effect sizes and possible working mechanisms. As a confounding factor, a placebo effect is considered as a specific type of reactivity effect that should be distinguished from other confounding factors like history, spontaneous recovery, or statistical regression.
Prevalence
Authors: Grietens Hans
Published: September 2014

Prevalence 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.
Multiple Informants
Authors: Saudino Kimberly J.
Published: September 2014

The use of multiple informants permits a better understanding of the behavior being examined. Quantitative genetic models allow the researcher to examine genetic and environmental overlap between different informants' ratings, and test assumptions regarding whether different informants assess the same phenotype and the presence of rater biases.
State Dependence
Authors: Skrondal Anders , Rabe‐Hesketh Sophia
Published: September 2014

State dependence occurs if the current outcome of a dynamic process depends on prior outcomes. It is crucial to distinguish between true and spurious state dependence.
Intervention Analysis
Authors: Brockwell P. J.
Published: September 2014

An outline is given of the intervention model of Box and Tiao and the way it can be applied to the modeling of changes in level of a time series due to interventions at specified times during the period of observation of the series. An example illustrating the effect of seat‐belt legislation on deaths and serious injuries on UK roads is included.
Concordance Rates
Authors: Oord Edwin J. C. G.
Published: September 2014

In genetic studies of medical conditions, pairwise or probandwise concordance rates are often calculated to study the relative importance of genes and environment.
Common Pathway Model
Authors: Rijsdijk Frühling
Published: September 2014

While traditional multivariate statistical methods can describe correlational patterns of a set of variables (e.g., psychiatric symptoms or problem behaviors in children), they cannot provide insight into why certain symptoms or behaviors tend to co‐occur in a population. This can be achieved using methods of
multivariate genetic analysis
. In multivariate genetic models, two main classes can be distinguished, which differ in the way the common factors are assumed to influence the different observed variables. The way unique factors influence the observed variables is the same in both classes. The first class of models is referred to as common pathway or psychometric models. Common genetic and environmental factors influence all observed variables via a single psychometric factor, or underlying latent liability. This factor is identical to the factor derived from higher order phenotypic factor analyses, with the difference that twin data allows estimation of the relative importance of genetic and environmental effects of this factor. The second class of models is referred to as
independent pathway models
or biometric models, where the common genetic and environmental factors influence the observed variables directly, without an intermediate higher order factor. As a result, the common genetic and environmental factors do not necessarily cause similar groupings of variables.
Clustered Data
Authors: Fitzmaurice Garrett M.
Published: September 2014

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.
Nonshared Environment
Authors: Towers Hilary , Neiderhiser Jenae M.
Published: September 2014

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.
Family History Versus Family Study Methods in Genetics
Authors: Burke Ailbhe , McGuffin Peter
Published: September 2014

Family history methods, where the familiality of a trait or disorder is explored by taking a history from one or more informants, are less costly and time‐consuming than family studies where a direct interview and examination of all available relatives of an index case is carried out. However family studies have a higher sensitivity. Studies based on families alone cannot distinguish between the effects of shared genes and shared environment.
Community Intervention Studies
Authors: Murray David M.
Published: September 2014

Community intervention trials randomize whole communities to conditions and observe residents of those communities to assess the effects of an intervention. Community intervention trials face special problems of design and analysis that must be considered to ensure adequate power and valid results. This article provides a brief review of these issues.
Clinical Psychology
Authors: Treat Teresa A. , Weersing V. Robin
Published: September 2014

Rapid advancement in the understanding and amelioration of complex clinical phenomena places heavy demands on clinical scientists for thoughtful articulation of theoretical models, methodological expertise, and statistical rigor. This article provides a descriptive and prescriptive overview of the use of statistics in clinical psychology. Five classes of research questions that clinical psychologists commonly address are described: the definition and measurement of constructs, the evaluation of group differences, the prediction of group membership, the examination of a network of constructs, and the evaluation of research questions across studies or data sets. A set of four principles that underlie the use of statistics in clinical research are described: the importance of theory to the rapid advancement of clinical science; the centrality of model specification, evaluation, and comparison to clinical research; the need for researchers to master research design and the mechanics of statistical techniques; and the limitations of null‐hypothesis statistical testing (NHST).
Inbred Strain Study
Authors: Wade Claire
Published: September 2014

Genetic contributions in complex trait analyses can be controlled or removed by the use of inbred strains as the test subjects. To be regarded as inbred strains, the mammals concerned must have been subjected to at least 20 generations of full‐sib mating (the expected inbreeding coefficient will be 98.6% or greater). Variation in the test scores observed within inbred strains will be of either environmental (treatment) origin or otherwise caused by random experimental error, since genetic variation is largely absent.
Quantitative Methods in Personality Research
Authors: Fraley R. Chris , Marks Michael J.
Published: September 2014

The science of personality documents and explains ways that people differ with respect to their thoughts, feelings, social relationships, and experiences. Quantitative methods such as factor analysis played an important role in facilitating the discovery of the fundamental dimensions of personality. Recent developments in the application of multiple regression, structural equation modeling, hierarchical linear modeling, item response theory, and taxometrics allow personality researchers to test increasingly sophisticated models of personality processes.
Sibling Interaction Effects
Authors: Boomsma Dorret I.
Published: September 2014

Human beings often grow up with siblings and the behavior of one sibling may influence the behavior of the other sibling. In such cases, part of the ‘environment’ of individuals is predictable from the genotypes of their siblings. To detect sibling interactions, which are based on
genotype
, the variances and covariances of siblings reared at different densities (e.g., twins and singletons) or siblings of different degrees of genetic relatedness (e.g., mono‐ and dizygotic twins) may be compared. In the classical
twin design
, sibling interaction effects are suggested by a difference in phenotypic variance between mono‐ and dizygotic twins, and a pattern of twin correlations that is inconsistent with additive genetic influences.
Comorbidity
Authors: Rhee S. H. , Hewitt John K. , Corley R. P. , Stallings M. C.
Published: September 2014

Comorbidity between psychiatric disorders often exceeds the rate expected by chance alone. Increased knowledge regarding the causes of comorbidity between two psychiatric disorders may have a significant impact on research regarding the classification, treatment, and etiology of both of disorders. We examined the validity of common methods used to test alternative comorbidity models.
Time Series Analysis
Authors: Brockwell P. J.
Published: September 2014

The 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.
Interrupted Time Series Design
Authors: Ferron John , Rendina‐Gobioff Gianna
Published: September 2014

Interrupted time‐series designs allow researchers to examine the effect of an intervention. Multiple observations are obtained prior to the intervention to establish a baseline. Multiple observations are also obtained after the intervention. Effects are demonstrated when the observations after the intervention deviate from expectations derived from baseline projections.