bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural Equation Modeling: Latent Growth Curve Analysis
Authors: Willett John B. , Bub Kristen L. 
Published: September 2005

Abstract
In this chapter, we use an example of panel data on the longitudinal development of reading in children to introduce the latent growth curve analysis. Latent growth modeling originates in a simple mapping of the standard multilevel model for change onto the general model available for covariance structure analysis (CSA). Here, we illustrate how the latent growth modeling approach can be enacted by using the CSA Y‐measurement model to contain the level‐1 individual growth model and the CSA structural model to contain the level‐2 model for interindividual differences in change. Because this mapping is possible, all fixed and random parameters in the multilevel model for change can be estimated by fitting the corresponding CSA model to data using standard structural equation modeling software (such as LISREL). Latent growth curve analysis is important because it capitalizes on the intrinsic flexibility of the general CSA model to test interesting and complex hypotheses about change, many of which are either more difficult or impossible to test using standard multilevel modeling approaches.
Structural Equation Modeling: Mixture Models
Authors: Vermunt Jeroen K. , Magidson Jay 
Published: September 2005

Abstract
This article discusses a modeling framework that links two well‐known statistical methods: structural equation modeling (SEM) and latent class or finite mixture modeling. Mixture SEM can either be viewed as a refinement of multivariate normal (MVN) mixtures, where the within‐class covariance matrices are smoothed according to a postulated SEM structure, or as a form of multiple group SEM, in which the group variable is unobserved. After introducing standard MVN mixtures, it is shown how the SEM framework can be used to restrict the class‐specific means and covariances. Furthermore, parameter estimation, model testing, and software is discussed, and an empirical example is provided.
Structural Equation Modeling: Nontraditional Alternatives
Authors: Rigdon Edward E. 
Published: September 2005

AbstractA 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.
Sensitivity Analysis in Observational Studies
Authors: Rosenbaum Paul R. 
Published: September 2005

AbstractIn an observational study, subjects are not randomly assigned to treatment or control, as they would be in a controlled clinical trial. As a consequence, differing outcomes in treated and control groups may not reflect effects of the treatment, but rather some way the groups were not comparable prior to treatment, that is, some hidden bias. A sensitivity analysis appraises how the conclusions of a study might be altered by hidden biases of various magnitudes.
Sequential Testing
Authors: Coad D. S. 
Published: September 2005

Abstract
The use of sequential methods in clinical trials dates back to the 1940s. With these methods, the accruing data are inspected periodically in order to assess whether the trial should be continued. Trials can be stopped when there is convincing evidence of a treatment effect or they can be stopped for futility. The use of sequential methods can reduce the numbers of patients in the trial compared with a design with a fixed sample size.
Sex‐Limitation Models
Authors: Eley Thalia C. 
Published: September 2005

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.
Shared Environment
Authors: Dick Danielle M. 
Published: September 2005

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.
Sibling Interaction Effects
Authors: Boomsma Dorret I. 
Published: September 2005

Abstract

Human beings often grow up with siblings and the behavior of one sibling may influence the behavior of the other sibling. In such cases, part of the ‘environment’ of individuals is predictable from the genotypes of their siblings. To detect sibling interactions, which are based on
genotype
, the variances and covariances of siblings reared at different densities (e.g., twins and singletons) or siblings of different degrees of genetic relatedness (e.g., mono‐ and dizygotic twins) may be compared. In the classical
twin design
, sibling interaction effects are suggested by a difference in phenotypic variance between mono‐ and dizygotic twins, and a pattern of twin correlations that is inconsistent with additive genetic influences.
Signal Detection Theory
Authors: Wixted John T. 
Published: September 2005

Abstract

Signal‐detection theory is a framework for understanding the process of decision making under conditions of uncertainty. Its essential claim is that subjective evidence for the presence or absence of a stimulus is a continuous variable. That is, evidence comes in degrees and is never entirely present or entirely absent even though the stimulus itself is. Although the average amount of subjective evidence is higher on trials in which the stimulus actually is present, trial‐to‐trial variability is such that no evidence value perfectly distinguishes stimulus‐present trials from stimulus‐absent trials. As such, the observer is assumed to select a criterion evidence value above which the stimulus is declared to be present and below which it is declared to be absent. This way of thinking sheds light on an extremely wide range of decision problems ranging from auditory detection to the detection of a psychiatric condition, and it offers a measure of discrimination (
d
′) that, unlike percent correct, is theoretically uncontaminated by response bias.
Simple Random Assignment
Authors: Thach Chau , Berger Vance W. 
Published: September 2005

Abstract
There are two types of simple random assignment – unrestricted random assignment and the random allocation rule. Unrestricted random assignment occurs when each subject in a study is assigned to a treatment with a fixed probability, completely independent of all previous assignments in the study. The total sample size does not need to be known in advance for this procedure. In contrast, the total sample size does need to be known in advance when using the random allocation rule, which occurs when a randomly chosen subset of subjects in a study is assigned to one treatment group and the other subset is assigned to the other treatment group. Unrestricted random assignment is the easiest and best way to minimize various biases, such as selection and accidental bias. However, it can also result in substantial imbalance if the sample size is small or in the early stages of the study. The random allocation rule also has little selection (if the assignment for all subjects is done simultaneously) and accidental bias.
Simple Random Sampling
Authors: Berger Vance W. , Zhang Jialu 
Published: September 2005

Abstract
When a census of the entire population of interest is difficult to obtain, a sample is often used. There are many sampling designs that can be used to obtain a sample that would hopefully be representative of the population, and among these sampling designs, several allocate the same inclusion probability to each unit in the population. If each unit in the population has the same inclusion probability and all the units are independent, then we have a simple random sample. Another way to formulate this is to state that each sample consisting of a given number of units has the same probability of becoming the selected sample.
Simple V Composite Tests
Authors: Macdonald Ranald R. 
Published: September 2005

Abstract

If a null hypothesis takes the form θ =
c
, where c is a constant and θ is the only unknown parameter in the supposed chance probability distribution, then this hypothesis is called a simple hypothesis. Where the chance distribution has more than one unknown parameter, the null hypothesis is said to be composite. Often, but not always, composite hypotheses are phrased in terms of several parameters. Examples include θ
1
=
c
1
, θ
2
=
c
2
, …θ

k

=
c

k

or θ
1
= θ
2
= θ
3
= ⋯ = θ

k

. Where this is the case, the overall null hypothesis can be split into separate testable component hypotheses and this raises multiple testing issues.
Simulation Methods for Categorical Variables
Authors: von Weber Stefan , von Eye Alexander 
Published: September 2005

Abstract
The results of a simulation study on the performance of measures for the analysis of 2 × 2 tables are reported. The simulations included 11 measures for 2 × 2 tables: Pearson's?, the standard normal z, the log‐odds ratio, the log‐linear interaction, Goodman's (1991) weighted log‐linear interaction, Vogel's z, the binomial test, Lehmacher's (1981) asymptotic hypergeometric test, Perli, Hommel, and Lehmacher's (1985) asymptotic test, Lindner's (1984) exact hypergeometric test, and Lausch and von Weber's (2002) adaptation of Dunkl and von Eye's (1990) test. The factors varied in the simulations were (1) type of sampling distribution, (2) sample size, (3) strength of association in the 2 × 2 table, (4) the symmetry of the distribution in the 2 × 2 table, and (5) the nominal a. Results suggest that the distribution of these 11 tests is very near the normal under all conditions. Of the five factors, only the type of the sampling distribution has no strong effects on the ß‐curves of the 11 measures. Finally, it was found that the 11 measures respond differently to the five factors such that the rank order of performance varies with the simulated conditions.
Simultaneous Confidence Interval
Authors: Dracup Chris 
Published: September 2005

Abstract
When a number of parameters are to be estimated at the same time, simultaneous confidence intervals are employed to control the probability that all the intervals to be constructed will contain their true parameter values. If Tukey's method is used to calculate simultaneous 95% confidence intervals for the differences between all pairs of population means in a multigroup experiment, then, in the long run, all the intervals so constructed will contain the true population mean differences for 95% of experiments. Scheffé's method allows the construction of simultaneous confidence intervals for more complex contrasts between means.
Slicing Inverse Regression
Authors: Chen Chun‐Houh 
Published: September 2005

AbstractSliced 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)
Skewness
Authors: Wuensch Karl L. 
Published: September 2005

Abstract
A skewed distribution is one that has an asymmetric tail on the left (negative) or the right (positive) side. Skewness is most commonly defined in terms of the third moment about the mean. Measures of skewness may alert the behavioral researcher to the need to investigate outliers, consider using the median rather than the mean, transform the data, and/or choose analytic techniques that do not assume normality.
Social Validity
Authors: Kazdin Alan E. 
Published: September 2005

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.
S
pearman's Rho
Authors: Kornbrot Diana 
Published: September 2005

Abstract

Spearman's rho,
r

s

, is a correlation measure of association based on ranks of scores rather than raw scores. It is applicable if either or both variables are direct rankings or only ordinal. It is also applicable when both variables are metric but not normally distributed, or when the relation between the variables is monotonic but nonlinear.
Sphericity Test
Authors: Keselman H. J. 
Published: September 2005

Abstract

Classical test statistics, such as the usual
F
test, for the analysis of repeated measures or longitudinal treatment effects, will provide accurate assessments (i.e., correct Type I error probabilities) of null hypotheses when their assumptions of independence, normality, and homogeneity hold. Homogeneity in this context is referred to as sphericity or circularity. This paper enumerates the validity assumptions for simple‐order and higher‐order repeated measures designs, as well as discussing why for many behavioral science applications sphericity will not be satisfied, and concludes by noting procedures that researchers can adopt to circumvent the biasing effects of nonsphericity.
Star and Profile Plots
Authors: Everitt Brian S. 
Published: September 2005

Abstract
Star and profile plots are graphical displays for multivariate data. They consist of an array of small individual plots, each corresponding to a single multivariate observation. In any individual plot, every variable is represented by a line whose length is proportional to the value of the variable. In a star plot, the lines form spokes radiating from a central point, while in a profile plot, they are vertical spikes. In each case, the ends of the lines are connected across variables.
These displays can provide useful insight into the structure of the data, especially when groups of plots with similar shapes, or particular individuals that seem different from the rest, can be identified.
Test Translation
Authors: Hershberger Scott L. , Fisher Dennis G. 
Published: September 2005

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)
Decision Making Strategies
Authors: Ranyard Rob 
Published: September 2005

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.
Stratification
Authors: Berger Vance W. , Zhang Jialu 
Published: September 2005

Abstract
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.
Structural Equation Modeling and Test Validation
Authors: Zumbo Bruno D. 
Published: September 2005

AbstractThe use of structural equation modeling in validity research is described and demonstrated in the context of an example wherein a researcher is providing construct and predictive validity evidence for inferences made from scores on the Center for Epidemiologic Studies Depression (CES‐D) scale. In this light, current concepts of validity are briefly overviewed and contrasted with those found in many research methods texts. In addition, two special cases of the generalized linear structural equation model, confirmatory factor analysis and multiple indicators multiple causes models for ordinal items, that are particularly well suited for measurement validity research, are introduced.
Structural Equation Modeling: Overview
Authors: Schumacker Randall E. 
Published: September 2005

AbstractStructural equation modeling (SEM) software programs analyze theoretical models using a sample variance–covariance matrix, although a correlation matrix with means and standard deviations for the observed variables can be input because these values can be used to create a variance–covariance matrix. A LISREL‐SIMPLIS program is given below to illustrate how the theoretical model and sample data are included in a SEM program.
Structural Zeros
Authors: Berger Vance W. , Zhang Jialu 
Published: September 2005

Abstract

Empty or zero cells in a contingency table can be classified as structural zeros or random (sampling) zeros. A cell with a structural zero has an expected value of zero, which means that not only did no observations in the data set at hand fall into that cell, but in fact that no observation
could
fall into that cell. Structural zeros are not part of the data. Therefore, they do not contribute to the likelihood function or model fitting. A contingency table containing structural zeros is, in some sense, an incomplete table. The usual χ
2
test cannot be applied directly.
Subjective Probability and Human Judgement
Authors: Ayton Peter 
Published: September 2005

AbstractPsychological research on Subjective Probability has used a variety of strategies to investigate how reliable people are at judging probabilities. The issue is of considerable practical importance as all sorts of critical decisions depend on assessment of the likelihood of events for which there is neither a method for mathematically computing its probability nor actuarial data for measuring it empirically. Since the development of Decision theory, a plethora of management technologies have emerged that can take advantage of likelihood information for decision support. While the concept of subjective probability remains a theoretically contentious notion for some, people can and do express and request judgments of the likelihood of uncertain events. Here, research investigating the validity of subjective probabilities – judged likelihood – is reviewed.
Summary Measure Analysis of Longitudinal Data
Authors: Everitt Brian S. 
Published: September 2005

Abstract

There are a variety of methods of analysis for longitudinal data, but one of the most straightforward is the use of summary measures that essentially reduce the problem from multivariate to univariate. Once a suitable summary measure has been chosen, analysis usually involves simple techniques such as
t
Tests, analysis of variance, or their nonparametric analogues.
Survey Questionnaire Design
Authors: Tourangeau Roger 
Published: September 2005

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.
T
au‐Equivalent and Congeneric Measurements
Authors: Berge Jos M. F. Ten 
Published: September 2005

Abstract
To evaluate the reliability of a test, one needs a parallel test. When parallel tests are absent, one option is to rely on hypotheses such as that the test parts are essentially tau‐equivalent or congeneric, having only one factor in factor analysis. Such hypotheses are unrealistic, except that a test may be congeneric when there are only three test parts; in that case, however, it is impossible to decide what the number of factors really is. A preferable option is to use lower bounds to reliability, such as coefficient alpha. The Jackson & Agunwamba greatest lower bound (glb) is better than alpha but has a sampling bias problem. Bias correction methods are still under study. Nevertheless, when compared to reliability estimates derived from the hypothesis of congeneric tests, the glb is already to be preferred, because these estimates behave very similarly to glb, and display the same sampling bias but do not rely on the hypothesis of a congeneric test