bluuebunny/crossref_metadata_2025 huggingface dataset:
Misclassification Rates
Authors: Krzanowski Wojtek J.
Published: September 2014

This article surveys the ways in which the error rate incurred by a classifier, that is, the proportion of future incorrect classifications, can be estimated. Theoretically derived rates depend heavily on distributional assumptions; so, in practice, data‐based estimates are generally preferred. Methods based on resubstitution, test‐set estimation, cross‐validation, jackknifing, and bootstrapping are described. The phenomenon of tuning the classifier by using one of these methods to optimize its performance is also explained, and the dangers of overoptimism in assessing error rates of a tuned classifier are highlighted. Interval estimation of error rates is briefly discussed, and an illustrative example is provided.
Principal Components and Extensions
Authors: Michailidis George
Published: September 2014

In this paper, we discuss some extensions of principal components analysis. We focus in particular on extensions that deal with categorical multivariate data. We also briefly discuss various approaches that try to enable the techniques that handle nonlinearities in the data.
Structural Equation Modeling: Nontraditional Alternatives
Authors: Rigdon Edward E.
Published: September 2014

A variety of techniques exist for modeling structural relations in situations where researchers' goals or data do not fit the demands of conventional Structural Equation Modeling. Researchers whose theory or data do not meet the rigorous standards of conventional Structural Equation Modeling (SEM) may employ Partial Least Squares (PLS), a technique based on principal components rather than common factors. Researchers seeking to find models, rather than testing a prespecified model, might employ Tetrad. Confirmatory Tetrad Analysis (CTA) allows researchers to test structural equation models without estimating model parameters. HyBlock conducts structured exploratory factor analysis, allowing researchers to model structural relations based on observed variables that are factorially complex.
Within Case Designs: Distribution Free Methods
Authors: Lix Lisa M. , Keselman H. J.
Published: September 2014

In within‐case designs, subjects are measured repeatedly over time or across multiple experimental conditions. Univariate and multivariate parametric procedures for testing within‐case effects may lose statistical power under departures from a multivariate normal distribution. Nonparametric procedures based on rank scores or bootstrapping are useful alternatives for testing within‐case effects when the data are skewed or have heavy tails.
Confidence Intervals: Nonparametric
Authors: Garthwaite Paul H.
Published: September 2014

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.
Nonparametric Correlation (tau)
Authors: Howell David C.
Published: September 2014

Kendall's correlation coefficient (τ) for ranked data, often known as Kendall's tau, is based on the number of inversions in a set of ranks. Data on
X
and
Y
are replaced by their ranks, and the correlation (τ) between the ranks is computed.
Rank Based Inference
Authors: Hettmansperger T. P. , McKean J. W. , Sheather S. J.
Published: September 2014

The use of ranks has a long history stretching back to 1945 with the seminal work of Wilcoxon. Early nonparametric rank tests were developed for the basic one‐ and two‐sample designs and then extended to the one‐ and two‐way layouts. These tests are popular because of their nonparametric property along with their good efficiency and robust properties. In this entry, we describe and illustrate how to extend rank‐based methods to the general linear model including regression, analysis of variance, and analysis of covariance. We also describe a coefficient of determination that is useful when assessing the fit of a model to the data.
Nonparametric Item Response Theory Models
Authors: Sijtsma Klaas
Published: September 2014

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.
Tukey Quick Test: Examples
Authors: Sawilowsky Shlomo
Published: September 2014

A distribution‐free test of the equivalence of two population medians. Because of its ease of computation, it is sometimes known as Tukey's Quick Test.
Pitman Test: Introduction
Authors: Lunneborg Clifford E.
Published: September 2014

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.
Sign Test: Basic
Authors: Sawilowsky Shlomo
Published: September 2014

Arguably the oldest distribution‐free test, the Sign test is used either in the one‐sample context to test a hypothesis about a population median or in the paired sample context to test a hypothesis about the equality of two population medians.
Kendall's Tau: Basic
Authors: Kornbrot Diana
Published: September 2014

Kendall's tau, τ, is a correlation measure of association based on ranks of scores rather than raw scores. It is based on the number of swaps needed to put the second variable into exactly the same or exactly the opposite order to the first variable. 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. Methods for calculating τ and procedures for hypothesis testing are provided. Kendall's τ is compared with Spearman's ρ and Pearson's
r
.
MEstimators of Location
Authors: Wilcox Rand R.
Published: September 2014

An approach to developing measures of location is to search for some value, sayc, that is close to the values in a sample, sayX1, …,Xn. If we measure closeness with the sum of squared differences between each of thenobservations andc, this leads to takingcto be the sample mean. But the sample mean can perform poorly in terms of achieving a small standard error when sampling from nonnormal distributions. M‐estimators attempt to correct this problem by replacing squared error with some other measure of closeness. Certain M‐estimators have standard errors that compete well with the sample mean under normality, but they continue to perform well in situations in which the sample mean performs poorly.
Normal Scores & Expected Order Statistics
Authors: Lunneborg Clifford E.
Published: September 2014

Rank tests replace raw scores with their ranks prior to computing a test statistic and its reference distribution. This use of ranks is advocated where the normality of the raw score observations is in doubt. An alternative approach is to replace the raw scores with values that resemble observations from a normal distribution more closely than do the integer‐spaced ranks, while taking the order of the raw scores into account. Permutation tests that are carried out following such a rank‐preserving transformation of the data is called a normal scores test.
Median Test
Authors: Sawilowsky Shlomo , Lunneborg Clifford E.
Published: September 2014

The Median Test is a procedure for testing the null hypothesis that
k
 ≥ 2 independent samples have been drawn from populations with a common median.
Monotonic Regression: Basic
Authors: Leeuw Jan
Published: September 2014

Monotone regression is a technique for finding the best fitting monotone function in a scatterplot. We discuss Kruskal's up‐and‐down blocks algorithm.
Random Effects in Multivariate Linear Models: Prediction
Authors: Longford Nicholas T.
Published: September 2014

When randomness is interpreted as variation across hypothetical replications, predicting random effects may appear like aiming for a moving target. This contradiction is resolved by considering the conditional distributions of predictors given that the target is held constant over the replications. This entry focuses on predictors based on linear random‐effects models.
Multivariate Multiple Regression
Authors: Hartung Joachim , Knapp Guido
Published: September 2014

Multivariate regression is used to explain the relationship betweenp > 1 quantitative dependent variables andqquantitative explanatory variables. When a correlation structure among the dependent variables is present, a single multivariate regression is more efficient than regressions analyses for each dependent variable separately.
Power and Sample Size in Multilevel Linear Models
Authors: B. Snijders Tom A.
Published: September 2014

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.
Repeated Measures Analysis of Variance
Authors: Everitt Brian S.
Published: September 2014

Repeated measures data arise when each participant in a study has measurements of a response variable of interest made under several different conditions, on different occasions, and so on. A univariate analysis of variance approach can be used if the covariance structure of the repeated measurements satisfies a condition known as sphericity, otherwise a multivariate analysis of variance approach may be needed.
Analysis of Variance: Cell Means Approach
Authors: Kirk Roger E. , Bekele B. Nebiyou
Published: September 2014

The two forms of the cell means model are described: the unrestricted model and the restricted model. Computational procedures are presented for a completely randomized design, randomized block design, and completely randomized factorial design. Computational procedures also are presented for a completely randomized analysis of covariance (ANCOVA) design. The relative merits of the cell means model and the classical analysis of variance (ANOVA) model approaches to analysis of variance are discussed.
Standardized Regression Coefficients
Authors: Landis Ronald S.
Published: September 2014

Standardized regression coefficients reflect the standard deviation change in a criterion variable associated with a standard deviation change in a predictor variable. Standardized regression coefficients permit comparisons of predictor–criterion relationships across studies in which the variables have been measured using different units of measure.
Direct and Indirect Effects
Authors: Muller Dominique , Judd Charles M.
Published: September 2014

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.
Probits
Authors: Stefanescu Catalina , Berger Vance W. , Hershberger Scott L.
Published: September 2014

The 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.
Tolerance and Variance Inflation Factor
Authors: Miles Jeremy
Published: September 2014

The variance inflation factor (VIF) and tolerance are two closely related statistics for diagnosing collinearity in multiple regression. They are based on the
R
‐squared value obtained by regressing a predictor on all of the other predictors in the analysis. Tolerance is the reciprocal of VIF.
Confounding in the Analysis of Variance
Authors: Bogartz Richard S.
Published: September 2014

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.
Dropouts in Longitudinal Studies: Methods of Analysis
Authors: Little Roderick J.
Published: September 2014

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.
Interaction Effects
Authors: Aiken Leona S. , West Stephen G.
Published: September 2014

Interaction effects refer to the effects of particular unique combinations of variables on an outcome that would not be expected from their average effects. When two variables interact, the conditional effect of one variable at a particular value of the other variable depends on the specific value of the other variable. One variable moderates the effect of the other variable. When variables interact, the effects of the two variables in combination are nonadditive; their simultaneous effect is not the sum of their individual effects. Continuous and categorical variables may interact; a variety of patterns of interactions are possible.
High‐Dimensional Regression
Authors: Leeuw Jan
Published: September 2014

Very large data sets are becoming more and more common in statistics, even in social and behavioral statistics. Such data require new types of statistical techniques. We discuss some of the many adaptions of linear regression analysis to these highly multivariate situations.
Centering in Linear Multilevel Models
Authors: Leeuw Jan
Published: September 2014

This entry considers the effects of centering the predictors in a multilevel regression analysis. It is shown that grand mean centering of first‐level predictors does not change the model or the fit, and merely leads to a different parametrization. Within‐group centering of first‐level predictors does lead to an essentially different model, and not to a simple reexpression of the same model.
Analysis of Variance: Classification
Authors: Kirk Roger E.
Published: September 2014

The nomenclature and acronyms for analysis of variance (ANOVA) designs are described. The presentation focuses on four characteristics of the designs: layout of the designs, partition of the total sum of squares and degrees of freedom, hypotheses that can be tested, and advantages and disadvantages of the designs.
Least Squares Estimation
Authors: Geer Sara A.
Published: September 2014

The method of least squares is about estimating parameters by minimizing the squared discrepancies between observed data on the one hand, and their expected values on the other. It is commonly used in the context of a regression model, where one observes pairs of variables (
X
,
Y
), with
X
a covariable and
Y
a response variable. The expectation of the response variable
Y
given the covariable
X
is modeled as a function
f
β
(
X
) depending on parameters β.

We present an explicit expression for the least squares estimator of β in the linear regression model, and also give its covariance matrix. The calculations are illustrated with a numerical example. The F statistic for testing linear hypotheses on β is also given. We conclude with some extensions.
Eta and Eta Squared
Authors: Field Andy P.
Published: September 2014

Eta squared (η
2
) is an elegant and simple way to express an effect size (typically the effect of manipulations across several groups). Put simply, it is the ratio of variability explained by the manipulations (the group means) to the total variability. It is an accurate measure for the sample, but is a biased estimate of the effect in the population.
L
ord's Paradox
Authors: Holland Paul W.
Published: September 2014

Frederic Lord presents a statistical paradox regarding the analysis of pre‐post data often used in program evaluation. His paradox cast doubt on the value of certain uses of the analysis of covariance. The resolution of the paradox requires careful attention to the ambiguity of the implicit causal questions that underlie it.
Nuisance Variables
Authors: Mitchell Dayton C.
Published: September 2014

Extraneous variation in a dependent variable can be controlled using selection, stratification, and/or statistical adjustment. In experimental studies, control is used to increase statistical power, whereas in nonexperimental studies, control is used in an effort to reduce confounding effects of preexisting differences among groups.
Residual Plot
Authors: Miles Jeremy
Published: September 2014

In a regression analysis, a number of assumptions about the data must be made and tested. One of the most powerful ways to do this is to produce a range of graphs of the residuals from the regression. Valuable information may be gained from examining a histogram of the residuals, a plot of the residuals against time, and a plot of the residuals against the predicted values from the regression equation.
Slicing Inverse Regression
Authors: Chen Chun‐Houh
Published: September 2014

Sliced inverse regression (SIR) is a computationally simple and fast method for reducing the dimension of the explanatory variablesxin regression problems. SIR does the following: (a) it determines the dimension of a subspace spanned by the linear combinations ofxthat contains all information needed for describing the relationship betweenxand the output variableY; (b) it assesses the significance of the contribution from each input variable to dimension reduction; (c) it provides a foundation for further exploratory graphical analysis and nonparametric regression. (Color versions of the figures presented in this article can be obtained fromhttp://gap.stat.sinica.edu.tw)
Fixed Effect Models
Authors: Vandenberg Robert J.
Published: September 2014

A term associated with the application of analysis of variance in experimental designs, fixed‐effect refers to a type of independent variable or factor. Most researchers exploying experimental designs use fixed‐effects in that the levels of the independent variables are finite, known, and in most cases, manipulated by the researcher to be reasonable different to detect a difference on the dependent variable.
Linear Multilevel Models
Authors: Leeuw Jan
Published: September 2014

Multilevel models are regression models designed for observation units from different nested levels. Predictors at the various levels are combined into a single linear mixed model, in which some of the regression coefficients are random variables and others are fixed constants. The class of models is described, and examples from school effectiveness research and growth curve analysis are given.
Analysis of Covariance in a pretest‐posttest experiment
Authors: Huitema Bradley E.
Published: September 2014

The analysis of covariance is a model‐based method of reducing bias and controlling nuisance variation in experiments, quasi‐experiments, and observational studies. When appropriately used, it can facilitate the interpretation of group comparisons and greatly increase power relative to the corresponding analysis of variance model.
Intrinsic Linearity
Authors: Goldsmith L. Jane , Berger Vance W.
Published: September 2014

In some cases in which a response variable is related to one or more predictor variables through a nonlinear relationship, suitable transformations of the response variable, predictor variables, parameters, or various combinations can lead to a linear model, thereby enabling the use of the powerful and efficient theory of linear models for estimation and prediction. Care must be taken with the use of such transformations to ensure that the resulting linear model has an error term of the appropriate, additive form.
Growth Curve Modeling
Authors: Singer Judith D. , Willett John B.
Published: September 2014

We demonstrate the conceptual underpinnings and practical use of growth curve models (also known as
individual growth models
and
multilevel models for change
), which describe how a continuous outcome changes systematically over time.
Polynomial Model: Introduction
Authors: Goldsmith L. Jane , Berger Vance W.
Published: September 2014

A special case of the linear model, a polynomial model allows representation and estimation of curvilinear relationships while using the theory and computation efficiency of the linear model.
Partial Least Squares‐Background and Applications
Authors: Sampson Paul D. , Bookstein Fred L.
Published: September 2014

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.
Nonrandom Samples
Authors: Wainer Howard
Published: September 2014

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.
Stratification
Authors: Berger Vance W. , Zhang Jialu
Published: September 2014

When a population is composed of several subpopulations and the subpopulations vary considerably on the factors in which we are interested, stratification can reduce the variability of the estimates by dividing the population into relatively homogeneous subgroups, and treating each subgroup separately. This concept can be applied for stratified sampling (sampling from within each stratum) and stratified random allocation of subjects that need not constitute a random sample, stratified or not.
Telephone Surveys
Authors: Driskell Robyn Bateman
Published: September 2014

Telephone survey methodology is often the preferred approach to surveying as it is a valid and reliable means of collecting data. The advantages of telephone surveys include cost efficiency, reduced survey time, and the ability to reach a large, representative sample. With the recent technologies of random‐digit dialing and computer‐assisted telephone‐interviewing, telephone survey methodologies have enhanced and improved. While some limitations exist in telephone surveys, the advantages outweigh the disadvantages, resulting in an efficient and effective method for collecting data.
Adaptive Random Assignment
Authors: Berger Vance W. , Zhou YanYan
Published: September 2014

The primary objective of a comparative trial is to provide a precise and valid treatment comparison. Another objective may be to minimize exposure to the inferior treatment, the identity of which may be revealed only during the course of the study. Unrestricted randomization is not generally used because it is susceptible to chronological bias, and this would interfere with the first objective, the valid treatment comparison. Yet, too many restrictions lead to a predictable allocation sequence, which can also compromise validity. It can be a challenge to find the right balance of restrictions on the randomization, and sometimes a covariate‐adaptive design is used. When the second objective is considered as well, then the solution must necessarily be a response‐adaptive design that evolves depending on the responses of those enrolled.
Paradoxes
Authors: Berger Vance W. , Durkalski Valerie
Published: September 2014

A 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.
Selection Study (Mouse Genetics)
Authors: Henderson Norman
Published: September 2014

Artificial selection for the high and low expression of behavioral traits has long been successful. The apparent simplicity of selection experiments belies the fact that unless the effective breeding size of each parental generation is very large, genetic drift can add complications to genetic interpretations. Without replicate lines to estimate genetic variance due to random drift, the effects of drift on heritability estimates and as a possible source of spurious associations between the selected trait and other traits cannot be readily assessed.
Meta‐Analysis in Behavioral Science
Authors: Noortgate Wim , Onghena Patrick
Published: September 2014

A meta‐analysis combines or compares in a systematic and quantitative way the results from several studies in which a similar research question is investigated. If differences in research outcomes are entirely due to sampling variation, combining the outcomes results in more precise answers to the research questions. In case of systematic inconsistencies in study outcomes, a meta‐analysis offers the possibility to look for an explanation for the heterogeneity, by investigating whether study outcomes are related to one or more study characteristics. In this article, the historical and theoretical context of meta‐analysis and the successive steps in a meta‐analysis are described shortly, and several models, techniques and caveats are discussed.
Case Studies
Authors: Onghena Patrick
Published: September 2014

A case study is an intensive and rigorous empirical investigation of a contemporary phenomenon, usually referring to a single individual, within its real‐life context. Valid inferences from case studies are possible by connecting closely to theory, collecting systematic and objective data, using continuous assessment or observations during an extended period of time, looking at multiple cases to test tentative hypotheses, and applying formal data‐analytic techniques. Case studies may involve unique, critical, or revelatory cases, and may differ on several dimensions: from more quantitative to more qualitative, more or less descriptive, exploratory, or explanatory, involving one or multiple cases, and involving no or multiple subunits of analysis. Some common pitfalls in conducting case study research are described and references to good examples are given.
Mendelian Genetics Rediscovered
Authors: Dickins David
Published: September 2014

Three European botanists coincidentally claimed their independent ‘rediscovery’ in 1900 of Mendel's neglected 1865 paper, in which he derived what are still the classic principles of genetics, from breeding experiments with peas in his monastery garden. It is necessary to get a clearer picture of why Mendel was doing what he did, and how the ‘rediscoverers’, and others enthused by them, misinterpreted its significance in some important respects, leading to an eclipse of Darwinism, not to recover for a further two or three decades of dispute and reinterpretation, after which the complementarity of Mendel's work with Darwin's became established.
Allelic Association
Authors: Cardon Lon R.
Published: September 2014

Allelic association refers to the situation in which the distribution of a measured outcome (the trait) varies according to the distribution of one or more genetic variants (alleles). Associated alleles may be directly related to the trait in a causal or susceptibility relationship or may be indirectly related via intermediary markers. Association study designs often involve either cases and matched controls or small nuclear families. Studies of complex diseases have not met their anticipated success in the past, possibly because of small sample sizes and poor design for the small effect sizes that are becoming apparent for many traits. Current population‐based studies are underway that may help to identify new genetic loci for common diseases.
Microarrays
Authors: Schalkwyk Leonard C.
Published: September 2014

Microarray studies have often been viewed as a survey method for finding large gene expression differences between paired samples. Experimental designs amenable to more detailed statistical analysis are increasingly common, particularly with the advent of highly standardized, industrially produced arrays and gradually diminishing costs.
Relative Risk: Definition
Authors: Everitt Brian S.
Published: September 2014

This entry explains the difference between relative risk and absolute risk.
Software for Behavioral Genetics
Authors: Neale Michael C.
Published: September 2014

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.
Focus Group Techniques
Authors: Rodabough Tillman , Wiggins Lacey
Published: September 2014

Frequently misunderstood as a simple discussion group, weak by scientific standards and producing little valid data, focus groups, in fact, make contributions whether combined with more traditional research methods or standing alone as a unique technique.
Mail Surveys
Authors: Mangione Thomas W.
Published: September 2014

Mail surveys are much less costly to conduct than telephone surveys or in‐person surveys and therefore are an attractive method of data collection for researchers. However, in general, mailed surveys have suffered from low response rates which limits their applicability. Extensive research conducted over the past few decades has documented a variety of procedural elements, which if followed, can overcome low response rates. In addition to postage‐paid return envelopes, strong respondent letters, and clear promises of respondent confidentiality or anonymity, the two most important procedures to incorporate in the study design are using as many as three reminders and including financial incentives for participation. Additional elements such as personalization, prenotification, modest length, and utilization of soft deadlines also have been shown to increase response rates slightly. By following these procedures, data collected through mail surveys can be as valid as data collected through telephone or in‐person methods.
Sex‐Limitation Models
Authors: Eley Thalia C.
Published: September 2014

Sex differences are common in the behavioral sciences. In addition to modelling mean, prevalence and variance differences, behavioral genetic sex‐limitation models can identify the presence of both quantitative and qualitative sex differences. Quantitative sex differences refer to the fact that the proportion of variance due to genes, shared, and nonshared environment may differ between the sexes. Qualitative sex differences indicate whether the same or different genes or shared environment influence the two sexes. Models are described that test for both quantitative and qualitative sex differences in variation in the normal range, underlying liability of risk to disorder, and presence of an extreme group at one end of a distribution.
Twins Reared Apart Design
Authors: Segal Nancy L.
Published: September 2014

Twins reared apart provide direct estimates of genetic influence. Past and ongoing studies show that genetic effects on behavioral development are pervasive. Twins reared apart can be used to test hypotheses generated by a variety of theoretical perspectives.
Ascertainment Corrections
Authors: Neale Michael C.
Published: September 2014

Analysis of data from non‐random samples of the population presents special challenges. Likelihood‐based methods offer a simple and direct way to correct statistical analysis by re‐normalizing the probability density function so that the probability of all possible outcomes sums to unity. The same approach may be applied to the analysis of continuous, binary or ordinal measures. Knowledge of the mechanism used to ascertain the sample is essential. Three types of ascertainment of pairs are considered for binary outcomes, according to whether the probability of being ascertained given affected status (π) is 1.0, almost zero, or intermediate. The last case, in which some concordant affected pairs are ascertained through both members of the pair, while others are ascertained through only one member, is the most challenging to implement.
Coincidences
Authors: Everitt Brian S.
Published: September 2014

This article examines what we mean by coincidence and points to the fact that coincidences do happen, and rare events do occur.
De
F
ries–
F
ulker Analysis
Authors: Rende Richard , Slomkowski Cheryl
Published: September 2014

DeFries‐Fulker (DF) analysis involves a series of models that provide traditional estimates of genetic and environmental parameters for individual differences on a trait, along with unique parameters specific to the etiology of extreme levels of a trait. These models are presented along with reference to a new model‐fitting implementation of the DF approach.
Demand Characteristics
Authors: Munter Agnes
Published: September 2014

Demand 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.
Generalizability
Authors: Berger Vance W.
Published: September 2014

Researchers generally study a phenomenon in a specific setting, and then try to generalize the obtained results to other settings. The degree to which the results of a study can be generalized from one setting to other settings is referred to as ‘generalizability’. Generalizing information from observations is essential for synthesis of knowledge, and there are some commonly accepted general rules for making generalizations. Here, we will first present a set of such commonly accepted rules and discuss the applications of these rules in behavioral sciences. We will then argue that generalization is not a mechanistic or statistical process, but rather that it requires abstract thinking. Therefore, any set of rules for generalizing information is subject to many exceptions.
Hawthorne Effect
Authors: Gottfredson Gary D.
Published: September 2014

The term
Hawthorne effect
has come to refer mistakenly to an increase in worker performance resulting merely from research or managerial attention to a work group. References to a Hawthorne effect in describing research have also come to connote flawed research. The remarkable improvements in worker productivity in the Hawthorne relay‐assembly experiment probably occurred because workers (a) began to receive pay contingent on the number of relays assembled, (b) set personal goals of increasing their assembly rate, and (c) attended to information about their productivity. The separation of the experimental workers from other assemblers also removed a social obstacle to improved productivity. This led workers to learn to assemble more relays per hour. The Hawthorne researchers intended to study the influence of arrangements that reduced fatigue on productivity, but the study design failed to rule out learning as the mechanism leading to improved performance.
Mathematical Psychology
Authors: Chechile Richard A.
Published: September 2014

Mathematical psychology is a branch of theoretical psychology that uses mathematics as a tool for psychological measurement of latent processes as a means to characterize the regular behavioral patterns that emerge from experimental studies. An example of the measurement of latent processes is research that decomposes memory loss into separate components due to storage and retrieval changes. Other models in mathematical psychology provide a detailed description of the results from many memory experiments. Mathematical psychologists have addressed a wide range of psychological problems and have utilized a diverse set of mathematical tools.
Longitudinal Designs in Genetic Research
Authors: Price Thomas S.
Published: September 2014

A brief introduction to longitudinal designs in genetic research, covering aspects of experimental design and the theory and interpretation of longitudinal behavioral genetic models. The Cholesky decomposition and the simplex model are used as examples.
Decision Making Strategies
Authors: Ranyard Rob
Published: September 2014

Individual decision making is a form of problem solving with a focus on the need to choose among alternative courses of action. The goal‐state of a decision problem is usually ill‐defined and a matter of personal preference. A decision strategy is a sequence of mental operations and actions that resolve conflicts among the available options in either value, degree of risk, or both. In this entry, alternative strategies to resolve value conflicts are described, varying on two basic characteristics: compensatory versus noncompensatory, and attribute‐based versus alternative‐based. Two‐stage and multistage strategies, strategies for decisions involving risk, and some current issues in decision‐process research are discussed.
Social Networks
Authors: Wasserman Stanley , Pattison Philippa , Steinley Douglas
Published: September 2014

Network analysis is the interdisciplinary study of social relations and has roots in anthropology, sociology, psychology, and applied mathematics. It conceives of social structure in relational terms, and its most fundamental construct is that of a
social network,
comprising at the most basic level a set of
social actors
and a set of
relational ties
connecting pairs of these actors. The
nodes
or
members
of the network can be groups or organizations as well as people. Network analysis involves a combination of theorizing, model building, and empirical research, including (possibly) sophisticated data analysis. The goal is to study network structure, often analyzed using such concepts as density, centrality, prestige, mutuality, and role.
Sequential Decision Making
Authors: Vos Hans J.
Published: September 2014

The purpose of this entry is to describe optimal rules for sequential mastery tests in the context of education. In a sequential mastery test, the decision is to classify a student as a master, a nonmaster, or to continue testing and administering another random item. The framework of Bayesian sequential decision theory is used; that is, optimal rules are obtained by minimizing the posterior expected losses associated with all possible decision rules at each stage of testing. The main advantage of this approach is that costs of testing can be taken explicitly into account. For given maximum number of items to be administered, it is shown how the appropriate action can be computed at each stage of testing for different number‐correct score.
Co‐twin Control Methods
Authors: Goldberg Jack , Fischer Mary
Published: September 2014

The 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.
Neuropsychology
Authors: Suchy Yana
Published: September 2014

Neuropsychology is a scientific and clinical discipline that is concerned with understanding the brain‐behavior relationship and the application of this understanding to assessment of patients with brain dysfunction. This chapter surveys statistical applications of both the scientific and the clinical aspects of neuropsychology, including
t
Tests; univariate, factorial, and repeated measures analysis of variance; analysis of covariance; correlational analyses; meta‐analysis; factor analysis; multiple regression; discriminant function analysis; and the receiver operating characteristic curve. Different statistical methods have the ability to contribute to different circumscribed components of clinical neuropsychology practice.
Social Validity
Authors: Kazdin Alan E.
Published: September 2014

Social validity is a concept that is used in intervention research in which the focus is on treatment, prevention, rehabilitation, education, and, indeed, any area in which the goal is to produce change in human behavior and adaptive functioning. Social validity draws attention to whether the goals of treatment, the intervention techniques that are used, and the outcomes that are achieved are acceptable, relevant, and useful to individuals who are stakeholders, consumers, or recipients of the intervention. In clinical trials of treatment, judgments are made about the effects of the intervention based on statistical significance and magnitude of effect. Social validity alerts us to the issues about the applied value of the intervention, and whether the intervention has had a palpable impact and actually helped people in ways that are evident in everyday life.
Additive Genetic Variance
Authors: Posthuma Danielle
Published: September 2014

Additive genetic variance is the variance of the breeding values of all individuals in a population. The breeding value of an individual in a given population derives from the mean genetic value of the offspring that an individual would produce when it is mated to a number of individuals taken at random from the population.
Expectancy Effect by Experimenters
Authors: Rosenthal Robert
Published: September 2014

Experimenter expectancy effect is the term referring to the unintended effect of experimenters' expectations or hypotheses on the results obtained from their research participants. Experimentally induced expectations have been shown to influence the behavior of psychological experimenters so as to increase the likelihood that their research participants will respond so as to confirm the experimenters' hypotheses or expectations. Experimental evidence for the operation of this phenomenon is described, and strategies for the control of experimenter expectancy effects are outlined.
Generalizability Theory: Overview
Authors: Webb Noreen M. , Shavelson Richard J.
Published: September 2014

Generalizability (G) theory is a statistical theory for evaluating the dependability (reliability) of behavioral measurements. G theory estimates multiple sources of measurement error and permits decision makers to design a measurement procedure that minimizes error.
Compensatory Equalization
Authors: Onghena Patrick
Published: September 2014

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 2014

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.