bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive Random Assignment
Authors: Berger Vance W. , Zhou YanYan 
Published: September 2014

Abstract
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

AbstractA paradox is a statement that apparently derives self‐contradictory conclusions. While the consideration of paradoxes in statistics can be instructional, they are also important, because they often have implications that should be kept in mind when designing studies and analyzing data. This entry reviews some of the more common paradoxes encountered in statistics, and their implications.
Selection Study (Mouse Genetics)
Authors: Henderson Norman 
Published: September 2014

Abstract
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

AbstractA 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

Abstract
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

AbstractThree 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

Abstract
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.
Software for Behavioral Genetics
Authors: Neale Michael C. 
Published: September 2014

Abstract
Algorithms for computing heritability and other statistics of interest to quantitative geneticists have been available for almost 100 years. In the past 20 years there has been considerable growth in software development for behavior genetic studies, which was largely motivated by a desire to obtain maximum likelihood estimates and to compare how well alternative models or hypotheses fit the data. Initially these methods were implemented in purpose‐built programs of very limited utility, which were difficult to use and to modify. LISREL overcame these problems by providing a more general modeling interface, but this was also found to be limited for the analysis of data collected from relatives. Mx was developed to reduce or eliminate these shortcomings; having a matrix algebra interpreter it permits specification of complex models for both traditional genetic epidemiology such as twin studies, as well as for linkage and association studies, and permits specification of mixture distributions and alternate model fitting methods.
Mail Surveys
Authors: Mangione Thomas W. 
Published: September 2014

AbstractMail 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

Abstract
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.
Ascertainment Corrections
Authors: Neale Michael C. 
Published: September 2014

AbstractAnalysis 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.
De
F
ries–
F
ulker Analysis
Authors: Rende Richard , Slomkowski Cheryl 
Published: September 2014

Abstract
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

AbstractDemand characteristics come into being when research data are collected through exchange of information between human participants. It is this complex of unintended or unverifiable indications in the research situation that make the respondent believe he or she can guess the researcher's hypotheses or objectives. They are systematic errors, which form a threat to validity. In research, they can be traced by means of ‘quasi‐control’ strategies. It is hoped that this form of bias can be neutralized by restricting the interaction between researcher and respondent.
Generalizability
Authors: Berger Vance W. 
Published: September 2014

Abstract
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

Abstract

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

AbstractMathematical 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.
Decision Making Strategies
Authors: Ranyard Rob 
Published: September 2014

AbstractIndividual 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

Abstract

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

Abstract
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

AbstractThe co‐twin control method is an elegant research design that uses differences within twin pairs to examine the association between a putative environmental risk factor and an outcome variable. The design is extremely efficient for examining risk factor–outcome associations compared to unmatched designs. This is especially true when using monozygotic (MZ) twins who share 100% of their genetic material. Co‐twin control studies can be used in both the experimental and observational settings. Statistical analysis for continuously distributed outcome variables focuses on the within‐pair mean difference, while for dichotomous outcomes, the matched pair odds ratio is used. Whenever possible, it is informative to examine differences in the relationship between the risk factor–outcome association by zygosity; findings that are only present in dizygotic (DZ) pairs suggest that the observed association may be due to genetic influence on both the risk factor and outcome. We present an illustration of the analysis of a co‐twin control study, looking at the association between military service in Vietnam and posttraumatic stress disorder, with data from the Vietnam Era Twin Registry. A wide range of new regression modeling methods, which account for nonindependent observations, can be readily adapted for co‐twin control studies.
Neuropsychology
Authors: Suchy Yana 
Published: September 2014

Abstract

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

Abstract
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.
Expectancy Effect by Experimenters
Authors: Rosenthal Robert 
Published: September 2014

Abstract
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.
Compensatory Equalization
Authors: Onghena Patrick 
Published: September 2014

Abstract
Compensatory equalization is a threat to the treatment construct validity in intervention studies, in which comparison groups not obtaining the preferred treatment are provided with compensations that make the comparison groups more equal than originally planned. Compensatory equalization can seriously bias the estimation of treatment effects and should be avoided whenever possible, for example, by social isolation or masking of the comparison groups. If compensatory equalization cannot be avoided, then broadening the treatment effect construct seems the most viable option.
Compensatory Rivalry
Authors: Conrad Karen M. , Conrad Kendon J. 
Published: September 2014

Abstract
Compensatory rivalry is a potential threat to the construct validity of the cause in intervention studies. Compensatory rivalry exists when the study group not receiving the experimental treatment (i.e., intervention) feels disadvantaged, disappointed, or left out and decides to obtain a similar intervention on its own. It may or may not involve intentional competition or rivalry with the experimental group. While we are not likely to prevent compensatory rivalry from operating, we should be aware of its possibility, measure it, and if possible, statistically adjust for it. Minimally, we would want to consider its possibility in interpreting the study findings.
Fisherian Tradition in Behavioral Genetics
Authors: Dickins David 
Published: September 2014

AbstractR.A. Fisher did much to bring about the ‘Modern Synthesis’ of Mendelian genetics with Darwinian natural selection into a theory of evolution that worked. This was the fruit of his dual talent as a mathematician (particularly, but not exclusively, as a statistician) and a biologist. Though his training also in physics would have led him to welcome the subsequent transformation of parts of biology to ‘hard science’ status in the field of molecular and developmental genetics, his insights were in the domain of transmission genetics, acting in populations, from which he derived a cluster of fecund hypotheses that have been particularly influential in the field of behavioral biology/evolutionary psychology.
Free Response Data Scoring
Authors: Clauser Brian E. , Margolis Melissa J. 
Published: September 2014

Abstract
Computers offer the potential to extend assessment by delivering complex interactive item formats that require examinees to respond in ways that more closely approximate the ‘real world’ criterion behaviors of interest. However, for this expansion of assessment to be practically useful, it is necessary for the computer to both deliver and score the assessments. During the last two decades, these procedures have been the focus of considerable effort by numerous researchers. This entry describes the state of the art for computerized scoring of complex item formats. It begins with a presentation of the types of procedures currently in use. The entry then discusses the strengths and weaknesses of differing approaches, focusing on conceptual distinctions between approaches. The discussion concludes with comments about the future of automated scoring.
Cultural Transmission
Authors: Hershberger Scott L. 
Published: September 2014

Abstract
Cultural transmission refers to the transfer of the knowledge, values, and ideas within a population. It is also a nongenetic form of natural selection in which behavioral adaptations that arise from cultural influences are passed from one generation to the next. Importantly, cultural transmission occurs between an individual and the individual's conspecifics, both between generations and within generations. ‘Cultural transmission’ is not synonymous with ‘environmental influence’ – only ‘individuals’ are the relevant part of the environment critical to cultural transmission. The transfer of information is based on social learning specifically, and not on other types of learning such as behavioral reinforcement. Mathematical models of cultural transmission have been developed that emphasize either the similarities or the differences among individuals within a population. Cultural transmission may also be an important cause of correlated genotypes and environments.