bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multilevel and
SEM
Approaches to Growth Curve Modeling
Authors: Hox Joop , Stoel Reinoud D. 
Published: September 2014

Abstract
Growth curve models are used to model development explicitly as a function of time. Two techniques are commonly used to construct and analyze growth curve models: structural equation modeling, with distinct measurement occasions represented by separate variables, and multilevel regression modeling, with different measurement occasions represented as nested within individuals. Both approaches are essentially the same, but they differ in the way they can be extended into models that are more complex. An example is given, and estimation methods and software are briefly discussed.
Mediation
Authors: Kenny David A. 
Published: September 2014

AbstractA mediator explains, at least in part, a causal relationship between two variables. The total relationship between the two variables is due to a indirect effect, that part explained by the mediator, and due to an direct effect, that part not explained be the mediator. Strong assumptions must be made to estimate and test mediation. The inclusion of multiple variables complicates mediational analysis. Mediational analysis while seemingly simple is actually quite complex.
Marginal Models for Clustered Data
Authors: Fitzmaurice Garrett M. 
Published: September 2014

Abstract

Clustered data commonly arise in studies in the behavioral sciences. For example, clustered data arise when observations are obtained on children nested within schools. Longitudinal and repeated measures studies also give rise to clustered data. Marginal models provide a broad class of regression models that are suitable for analyzing diverse types of clustered data (e.g., continuous, binary, and count data). The distinctive feature of marginal models is that they focus on each response within the cluster separately, allowing dependence of the mean response on covariates but not on any random effects or other responses within the same cluster. By focusing on the population mean response, averaged over all clusters, the regression coefficients relating the mean response to covariates are said to have
population‐averaged
interpretations.
Transformation: Overview
Authors: Schmitt Neal 
Published: September 2014

Abstract
Transformations are systematic alterations to a set of scores that allow for application of various statistical procedures or better communication to readers. Transformations that often change the mean and standard deviation of a set of scores, but preserve the rank order and relative size of the distance between scores are linear; nonlinear transformations do not preserve these characteristics of the original data. Taking the square or square root or normalizing a set of scores are nonlinear transformations that make multivariate or parametric data analyses procedures more acceptable. Computing percentiles or T scores is often done to provide data in terms that are more easily understood by the statistically unsophisticated audiences.
Hill's Criteria of Causation
Authors: Goodman Karen J. , Phillips Carl V. 
Published: September 2014

Abstract

The term
criteria of causation
(or
causal criteria
) is often applied to Sir Austin Bradford Hill's (8) list of factors to consider before inferring causation from an observed association. This list is widely taught and cited, despite widespread cautions about the limitations of the list, by Hill and others, and despite lack of evidence that invoking such criteria is useful. This entry describes Hill's views on assessing causation and what others have since said on the subject of causal criteria.
Survey Questionnaire Design
Authors: Tourangeau Roger 
Published: September 2014

Abstract
Survey questionnaire design encompasses a variety of activities ranging from library research to formal experiments that estimate the reliability or validity of different versions of a questionnaire. Although handbooks with advice for survey questionnaire designers first appeared more than 50 years ago, it has only been during the last 20 years that researchers have attempted to provide a firm theoretical and empirical foundation for such guidelines, based on a cognitive analysis of the survey response process. The last 20 years or so have also seen an expansion in the methodological and statistical tools used in developing survey questionnaires; these tools include focus groups, cognitive interviews, field tests, behavior coding of respondent‐interviewer exchanges, reinterviews, and experiments.
Epistasis
Authors: Evans David M. 
Published: September 2014

Abstract
Biologically speaking, epistasis refers to the interaction between genes at different loci to produce effects which are different from that expected from the individual loci themselves. In quantitative genetic terms, epistasis refers to a deviation from additivity in the effect of alleles and genotypes at different loci with respect to their contribution to a quantitative phenotype. The proportion of variance due to epistasis may be estimated using careful breeding experiments in experimental organisms, but cannot be estimated using the classical twin design. It is possible to model epistasis explicitly in linkage and association analyses.
Genotype
Authors: Elston Robert C. 
Published: September 2014

Abstract
The genotype of an organism is its genetic make‐up, as opposed to its phenotype, or outward appearance. If the two alleles at a locus are the same, the genotype is homozygous; otherwise it is heterozygous. If a heterozygote has the same phenotypic distribution as one of the corresponding homozygotes, the allele in that heterozygote is dominant and the other allele is recessive with respect to that phenotype. A multilocus genotype is made up of two haplotypes. In the case of a quantitative trait, dominance corresponds to intralocus interaction, epistasis to interlocus interaction.
Number Needed to Treat: Definition and Controversies
Authors: Lesaffre Emmanuel 
Published: September 2014

Abstract
The number needed to treat (NNT) is the inverse of the difference between two probabilities. Clinicians prefer the NNT over classical measures like the odds‐ratio to evaluate the impact of a new treatment versus a control treatment. On the other hand, statisticians have pointed out that the NNT has undesirable statistical properties. Despite the dispute between clinicians and statisticians, the NNT is becoming increasingly important in Evidence‐based Medicine. The use of NNT will be illustrated with a medical example. Further, we will review the claims that have been made by the clinicians and the criticism of the statistical community.
Historical Controls and Clinical Trials
Authors: Berger Vance W. , Shafran Randi 
Published: September 2014

Abstract
Randomized trials, in which a single group of units is randomly allocated to two or more treatment conditions and then studied in parallel over time, is the gold standard methodology for reducing biases. Yet not every comparison is amenable to a randomized trial. For example, one cannot randomize subjects to their genders or races. Furthermore, even some comparisons that could theoretically be amenable to study with a randomized trial would not be, for ethical reasons. This would include studies of tobacco. Nonrandomized studies may still be parallel over time, in that all treatment groups are treated and studied over the same time interval. In some cases, though, one needs to use historical controls, and perform a before‐and‐after comparison. Strong conditions are required for such comparisons to be reliable.
Receiver Operating Characteristics Curves
Authors: Wright Daniel B. 
Published: September 2014

Abstract

The receiver operating characteristic (ROC) curve is the principal graphical device for signal detection theory (SDT). It is generally used when there is a right or a wrong answer and participants provide some measure of confidence in their responses. The ROC is a scatterplot between the hit rate and the false alarm rate. Exploring this space allows the researcher to consider the differential value of hits and correct rejections (or of misses and false alarms). This is where ROCs have a big advantage over the alternatives. The main alternative,
logistic regression
, is based on similar mathematics. Historically, SDT/ROCs assumed normal distributions, but this is very similar to the logistic distributions, and most statistical packages allow either. The choice of approach rests largely on the form of graphical communication the researcher wants to use. While logistic regression focuses on discrimination, in SDT the researcher explicitly differentiates two types of accuracy: sensitivity and specificity. The ROC curve is an excellent tool for comparing these.
Industrial/Organizational Psychology
Authors: Landis Ronald S. , Kaplan Seth A. 
Published: September 2014

Abstract

Industrial and organizational (IO) psychologists utilize a variety of statistical techniques in their research. A nine‐year review of two leading journals (
Journal of Applied Psychology
and
Personnel Psychology
) in the field reveals that correlational techniques appear in the largest percentage of studies. More complex methods such as structural equation modeling (SEM) and hierarchical linear modeling (HLM) appear in proportionally fewer studies. Representative examples of how various statistical techniques are used in IO research are provided, and possible reasons for the observed results are presented. In addition, IO psychologists' contributions to statistics and the role of statistics in IO theory development are discussed.
Correlation Issues in Genetics Research
Authors: Cherny Stacey S. 
Published: September 2014

Abstract

Correlation is of central importance in behavioral genetic research. A basic goal of behavioral genetic research is to partition variance in a trait into variance due to genetic differences among people (commonly referred to as
heritability
(or
h
2
), variance because of environmental influences shared among family members (shared environmental variance, or
c
2
), and nonshared environmental variance as a result of unique individual experiences (
e
2
) (
see
ACE Model
). This is accomplished by analyzing correlational structure within different familial relationships. Although in practice we perform
maximum‐likelihood
model‐fitting analyses on raw data and partition (unstandardized) covariance structure, examining familial correlations directly is the best way of quickly getting an idea of what is going on in the data.
Generalizability Theory: Estimation
Authors: Sanders Piet F. 
Published: September 2014

Abstract
Generalizability theory is a conceptual and statistical framework for the analysis and construction of measurement instruments. Among the most important concepts of generalizability discussed in the first section of this entry are the universe of admissible observations, universe and observed scores, random and fixed facets, crossed and nested designs, variance components, generalizability studies, decision studies, and generalizability coefficients. A discussion of the results of a generalizability study and a decision study of a crossed one‐facet random effects design and of a two‐facet crossed one‐facet random effects design is presented in the two following sections. A number of other designs that illustrate the versatility of generalizability theory are presented in the final section.
Educational Psychology: Measuring Change Over Time
Authors: Kulikowich Jonna M. 
Published: September 2014

Abstract
Educational psychologists use repeated measures analyses to study changes over time that reflect academic, cognitive, emotional, and social development of students. The purpose of this chapter is to review the basic principles and procedures of repeated measures in statistics, and, based on these principles and procedures, to describe important considerations of educational psychologists as they attempt to answer research questions. Testing of assumptions, estimating effect sizes and power, and current statistical advances in the study of how individuals' affect, behavior, and cognition change are discussed.
Twin Designs
Authors: Spinath Frank M. 
Published: September 2014

Abstract

The classical twin study compares the phenotypic resemblances of identical or
monozygotic
(MZ) and fraternal or
dizygotic
(DZ) twins. MZ twins derive from the splitting of one fertilized zygote and, therefore, inherit identical genetic material. DZ twins are first‐degree relatives because they develop from separately fertilized eggs and are 50% genetically identical on average. It follows that a greater within‐pair similarity in MZ, compared to DZ twins suggests that genetic variance influences the trait under study.
Reactivity
Authors: Onghena Patrick 
Published: September 2014

Abstract

Reactivity is a threat to the outcomes or treatments construct validity of observational and intervention studies that refers to the unintended reaction of participants on being observed or on being in a study. It is a threat to the
outcomes construct validity
if participants behave differently because they are aware that some aspect of their functioning is being observed or measured. It is a threat to the
treatments construct validity
if participants behave differently because they interpret the research or treatment setting in a way that makes the actual treatment different from the treatment as planned by the researcher. Because of the inevitable tendency of human participants to interact and give meaning to settings and their own and other's behavior, reactivity is a considerable risk to invalidity of both quasi‐ and randomized experiments in behavioral science.
Observational Study:Definition and Examples
Authors: Rosenbaum Paul R. 
Published: September 2014

AbstractAn observational study is an empiric investigation that attempts to estimate the effects caused by a treatment when it is not possible to perform an experiment. Random assignment of subjects to treatment or control in an experiment ensures that comparable groups of subjects are compared under alternative treatments. Without random assignment, in an observational study, the treatment and control groups may not be comparable prior to treatment, so that differing outcomes after treatment may not be effects caused by the treatment. Pretreatment differences that have been accurately measured are calledovert biases; they may often be controlled using analytical adjustments, such as matching, stratification, or covariance adjustment. Pretreatment differences that were not recorded are called hidden biases, and they pose substantial problems that must be addressed by other means. A well designed observational study permits detection of one or two of the most plausible hidden biases. The analysis of an observational study can measure the degree of sensitivity of conclusions to hidden biases of plausible magnitudes. Design strategies involving pattern specificity, such as Campbell's ‘multiple operationalism’ or Hill's dose‐response, may reduce sensitivity to hidden bias, and this possibility may be appraised using the design sensitivity of competing designs.
Gene‐Environment Correlation
Authors: Dick Danielle M. 
Published: September 2014

Abstract

Genetic and environmental effects are not independent sources of influence on behavior, but rather, genotype can influence an individual's exposure to certain environments, how that individual experiences the environment, and the degree of influence that certain environments exert. This phenomenon is called
gene‐environment correlation
. Three types of gene‐environment correlation are discussed: passive, evocative, and active. Examples of each type of gene‐environment correlation, and support for these effects from the literature are presented.
Dominance
Authors: Evans David M. 
Published: September 2014

Abstract

Dominance refers to the nonlinear interaction between alleles at the same locus. In the case of a discrete trait, if one allele masks the effect of the other allele at the same locus, we say that the first allele displays dominance over the second allele. In the case of a quantitative trait, the amount of dominance at a locus may be quantified by the dominance parameter
d
, which equals the deviation of the heterozygote's phenotypic value from the midpoint of the two homozygotes' phenotypic values. In the case of a single biallelic locus in Hardy–Weinberg equilibrium, the dominance variance is given by the formula (2
pqd
)
2
where
p
and
q
are the allele frequencies at the locus. The amount of dominance variance affecting a trait can be estimated using the classical twin design.
Evaluation Research
Authors: Lipsey Mark W. , Tidd Simon T. 
Published: September 2014

AbstractQuantitative methods are widely used in the evaluation of social programs. The most methodologically challenging type of evaluation attempts to determine if a program actually has the intended effects on the social conditions it addresses. Variations on experimental and quasi‐experimental research designs are used for this purpose, but their application to social programs involves many distinctive issues, problems, and pitfalls.
Shared Environment
Authors: Dick Danielle M. 
Published: September 2014

Abstract
The shared environment (also called common environment) refers to environmental influences that have the effect of making siblings more similar to one another. Shared environmental influences can include shared family experiences, shared peer groups, and sharing the same school and community. In general, there has not been strong evidence for shared environmental effects on many behaviors, particularly those measured in adults. Possible reasons for this are discussed. Shared environmental effects are evident in children and adolescents, but these effects generally decrease across the life span. New developments in behavior genetic methods have made it possible to specify shared environments of importance and to tease apart familial and nonfamilial sources of shared environmental influence.
Test Translation
Authors: Hershberger Scott L. , Fisher Dennis G. 
Published: September 2014

Abstract
Semantic and conceptual equivalence should characterize tests translated from one language to another. Semantic equivalence concerns the degree to which the ideas implied by the words and phrases of the original test have been captured by those in the test's translation. Various methods of confirming semantic equivalence include direct translation, translation/back‐translation, the ultimate test, the parallel blind technique, and the random probe technique. Semantic equivalence is most likely when the two test versions have been developed in tandem. Conceptual equivalence concerns the similarity of the construct measured by the two test versions. Methods for confirming conceptual equivalence tend to be more statistical than those methods used to confirm semantic equivalence, and include exploratory and confirmatory factor analyses, and item response theory (IRT)
Overdispersion
Authors: Dean Charmaine B. 
Published: September 2014

AbstractCount data analyzed under a Poisson assumption or data in the form of proportions analyzed under a binomial assumption often exhibit overdispersion, where the empirical variance in the data is greater than that predicted by the model. This entry illustrates how overdispersion may arise and discusses the consequences of ignoring it, in particular, the underestimation of standard errors of covariate effects. Straightforward methods for ascertaining whether overdispersion is evident are presented. Simple methods for incorporating overdispersion within the framework of quasi‐likelihood estimation are reviewed as well as generalized linear mixed models, which form a broad scheme for model‐based analysis when overdispersion is present.
Covariates
Authors: Sauerbrei Willi 
Published: September 2014

Abstract
The central role of covariates for several issues in clinical research and patient management is discussed. In clinical trials covariates are used to define the patient population, to allocate treatment in a randomized trial, to adjust the estimate of a treatment effect, and to define subgroups for the investigation of interactions. Furthermore, we will discuss the issue of categorization or determination of the functional relationship to describe the influence of a continuous covariate on an outcome of interest. Some issues of reporting and of the summary assessments of prognostic markers are discussed in the last section. (The issues of analyzing a prognostic marker study are discussed elsewhere.) To further increase and improve the use of covariate data of individual patients, it is important to have reliable measurements and to analyze the data and their interrelationships with the most suitable statistical approach. Often, this will require a close collaboration between clinicians and statisticians.
Subgroup
Authors: Wittes Janet 
Published: September 2014

AbstractRandomized clinical trials are usually designed to show an effect of treatment in the study population at large. Trials rarely have sufficient statistical power to measure reliably the effect of a treatment within a subgroup of that population. They are even less likely to provide reliable information on whether the effect of a treatment differs from subgroup to subgroup. This article defines various terms related to analysis of subgroups. It distinguishes between subgroups defined on the basis of baseline variables from “improper” subgroups defined on the basis of variables measured after randomization. It also discusses hypotheses defined a priori and those identified after the results have emerged. It emphasizes that random variation arising from small sample sizes can lead to unrealistically large or small observed effects and warns investigators to interpret findings from subgroups with caution.
Mother to Child Human Immunodeficiency Virus Transmission Trials
Authors: Shapiro David E. 
Published: September 2014

Abstract
Mother‐to‐child transmission (MTCT) of human immunodeficiency virus type 1 (HIV) can occur through three major routes: through the placental barrier during gestation, via contact with maternal bodily fluids during labor and delivery, and by ingestion of breast milk after delivery; most MTCT is believed to occur close to the time of or during childbirth. Prevention of MTCT (PMTCT) is one of the most successful areas in HIV clinical research. Interventions to prevent MTCT have been developed for two different settings: the United States and other developed countries with access to medications, medical infrastructure, and safe replacement feeding so that breastfeeding can be discouraged; and resource‐limited countries with limited access to medications and clean water, and where breastfeeding is critical. This article describes four important PMTCT randomized trials, including two trials of key interventions used in developed countries and two trials of interventions feasible in resource‐limited settings.