bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular and Genetic Characterization of Nicotiana glutinosa L. Chromosome Segments in Tobacco mosaic virus‐Resistant Tobacco Accessions
Authors: Lewis R. S. , Milla S. R. , Levin J. S.
Published: September 2005

Tobacco mosaic virus (TMV)‐resistant flue‐cured tobacco (Nicotiana tabacum L.) cultivars have been developed using the N gene derived from N. glutinosa L. Their adoption has been low, however, because of unfavorable linkage drag effects. Strategies to overcome this problem might include pursuit of alternative introgression events and/or use of molecular markers for selection against deleterious alien chromatin. Previous workers demonstrated the presence of a TMV‐resistance mechanism on more than one chromosome of the tobacco genome. The objectives of this research were to determine the relative genomic positions of TMV resistance loci in a set of 12 TMV‐resistant tobacco accessions and to use amplified fragment length polymorphism (AFLP) markers for characterization of this material with respect to linked alien chromatin. Five accessions were found to carry a TMV resistance gene on chromosome H. Seven accessions were found to carry a resistance factor on an alternative chromosome. Polymerase chain reaction results indicated that the N gene from N. glutinosa is responsible for resistance in all 12 accessions. A set of 168 AFLP markers specific to the N. glutinosa donor chromosome was identified and used to reveal variability among the 12 accessions for the relative amounts of N. glutinosa chromatin linked to the N gene. The relative propensity for crossing over within the alien segment when in different genomic positions was evaluated in BC1F1 families derived from three different accessions. Lines possessing the N gene on chromosome H may be of greater practical value because of relatively smaller introgressed alien segments and increased potential for obtaining crossover events within the segments.
Genetic Improvement of New Mexico Acala Cotton Germplasm and Their Genetic Diversity
Authors: Zhang J. F. , Lu Y. , Adragna H. , Hughs E.
Published: September 2005

The New Mexico cotton breeding program was established in 1926 and has been led by five generations of breeders and geneticists. The program has released more than 30 Acala 1517 cotton (Gossypium hirsutum L.) cultivars and numerous germplasm lines known for high fiber quality and Verticillium wilt (caused by Verticillium dahliae Kleb.) tolerance that have made substantial contributions to cotton breeding in the USA. The present project was initiated in 2003 to evaluate the genetic improvement of Acala 1517 cultivars and lines released over the past 75 yr in yield, boll size, seed index, lint percentage, fiber length, fiber strength, and micronaire. Their genetic divergence was also estimated by simple sequence repeat (SSR) markers. On the basis of the data available from annual yield trials, lint yield and lint percentage in Acala 1517 cotton have steadily increased since the 1930s, while boll size and seed index have gradually decreased since the 1960s. Fiber strength has been enhanced since the 1960s, which has been accompanied by steady increase in micronaire. However, fiber length in Acala 1517 cultivars tended to shorten from 31.0 to 30.0 mm from 1960 to 1990, whereas newly released Acala 1517 cultivars (Acala 1517‐95, 1517‐99, 1517‐02, 1517‐03, and 1517‐04) have fiber greater than 30.5 mm. Genetic distance among Acala 1517 genotypes ranged from 0.06 to 0.38 with an average of 0.18 on the basis of 189 SSR marker alleles, indicating a substantial genetic diversity among Acala 1517 cotton germplasm. Divergent germplasm introgression in the program has contributed to genetic diversity of Acala cotton germplasm and continuous genetic gain in Acala cotton cultivar improvement.
Soil Catena Sequences and Fire Ecology in the Boreal Forest of Alaska
Authors: Ping C. L. , Michaelson G. J. , Packee E. C. , Stiles C. A. , Swanson D. K. , Yoshikawa K.
Published: September 2005

This paper is the first to provide chemical, physical, and morphological properties of the soils in the boreal region of Alaska and to demonstrate the dominant effect of landscape attributes in soil formation. This study was conducted to characterize soils and landscape relationships in the boreal forest of Alaska. Sites representing major landform positions and vegetation communities were selected for study. Well‐drained, shallow to moderately deep Inceptisols occur on the drier and warmer south aspect slopes with thin (5–9 cm) organic horizons and loamy textures. Poorly drained Gelisols form on wet and cold north aspect slopes and valley floors with thick (18–52 cm) organic horizons and permafrost within 45 to 60 cm of the surface. On the latter, mineral horizons are reduced. Soils on the ridgetop show features of relic, nonsorted circle (a type of pattern grounds) that indicate a previous periglacial environment. The mineral soil on backslopes is shallow to moderately deep due to gelifluction and slope movement but deep toward toeslopes. Charcoal particles, commonly found within the soil profiles, attest to frequent fire events in the past. Fire has the greatest impact on soil properties on south aspect slopes because the organic horizons are thin and dry, and easily destroyed by fire. The underlying mineral horizons often develop hydrophobicity resulted from moderately and severe burn. Slope, aspect, and slope gradient are major controlling factors for the contrasting soil types along the catena sequences in the watershed.
Methane Oxidation in Forest, Successional, and No‐till Agricultural Ecosystems
Authors: Suwanwaree Pongthep , Robertson G. Philip
Published: September 2005

Methane oxidation in well‐aerated soils is a significant global sink for atmospheric methane. We examined the effects of soil disturbance (simulated tillage) and N‐fertilizer additions on methane oxidation in old‐growth forest, mid‐successional, and no‐till maize ecosystems in southwest Michigan, USA. We found highest oxidation rates in forest sites (about 30 μg CH4–C m−2 h−1 on average), with average rates in successional and agricultural sites about 75 and 12% of this, respectively. In the forest and successional sites a one‐time N‐fertilizer addition (100 kg NH4NO3–N ha−1) significantly suppressed oxidation for the several weeks that inorganic N pools were elevated. There was no effect of fertilizer addition in the agricultural site, where available N was already high and oxidation rates low. Soil disturbance by itself had no detectable effect on fluxes in any of the sites. Results confirm the overriding importance of elevated N for suppressing CH4 oxidation in managed and unmanaged ecosystems, and suggest further that recovery of CH4 suppression following agriculture is related to slow‐changing soil properties such as soil organic matter composition or microbial community structure.
Role of Mineral‐Nitrogen in Residue Decomposition and Stable Soil Organic Matter Formation
Authors: Moran Kelly K. , Six Johan , Horwath William R. , van Kessel Chris
Published: September 2005

The role of mineral fertilizer‐N inputs and N deposition to agricultural and natural ecosystems can affect plant residue decomposition and soil C processes. Yet it is still unclear whether residue‐N or mineral‐N is preferentially stabilized during the formation of soil organic matter (SOM). We undertook a 90‐d incubation of 13C15N‐labeled rice (Oryza sativa L.) straw residue and 15N‐(NH4)2SO4 under standard temperature and moisture conditions to determine: (i) the role of mineral‐N as an N source for stable SOM in the presence of residue‐N; and (ii) whether mineral‐N inputs can enhance sequestration of residue‐C. Soil C respiration was measured frequently and organic matter (OM) was fractionated into particulate organic matter (POM), humic acid, fulvic acid, and humin before and following the incubation. Stable C and N isotopic analyses were performed on CO2–gas samples and SOM fractions. We found significantly greater residue‐C was transformed into humin‐C with mineral‐N input suggesting that mineral‐N enhances residue decomposition and favors SOM formation. We found no preferential transformation of mineral‐N over residue‐N into SOM, but the two N sources together interact to alter each other's rate of accumulation in stable SOM. Our results suggest that mineral‐N inputs have a positive impact on the transformation of residue C into more stable SOM and that the combined addition of mineral N and residue enhance SOM formation.
Soil Property Analysis using Principal Components Analysis, Soil Line, and Regression Models
Authors: Fox Garey A. , Metla Roopa
Published: September 2005

Past research has attempted to relate surface characteristics of soils to reflectance from remotely sensed images to provide a means for quantifying spatial heterogeneity. Existing procedures have proven valuable, but no research has been performed to compare these techniques. The objective of this research was to compare existing methodologies, that is, principal components analysis (PCA), Chen et al.'s regression model, and the soil line Euclidean distance (SLED) technique, for quantifying spatial heterogeneity in soil surface organic matter (OM) and cation exchange capacity (CEC). The three existing techniques were compared using five bare soil images from three different silt loam to loam fields in the Midwest USA. At the same time as image acquisition, surface (upper 2.54 cm [1 in]) soil properties were measured in situ. Organic matter and CEC were highly correlated (R2> 0.70) to the first principal component (PC1) for three bare soil images, moderately correlated (R2> 0.40) for one image, and only slightly correlated (R2< 0.25) for the final image. The lower correlations were hypothesized to be because of the range in the soil OM and CEC and image exposure. Principal Component 1 accounted for approximately 95% of the total variance in all the fields; therefore, no correlation was observed between the upper 2.54 cm (1‐in) surface soil properties and the second, third, or fourth principal components (PC2, PC3, and PC4, respectively). All three techniques equivalently predicted OM and CEC. However, PCA does not require field‐specific regression or soil lines parameters. It is also suggested that PC1 can replace the soil line in a technique for identifying soil‐sampling locations.
Mineral Assemblage and Aggregates Control Carbon Dynamics in a California Conifer Forest
Authors: Rasmussen Craig , Torn Margaret S. , Southard Randal J.
Published: September 2005

Uncertainty about the effects of climate change on terrestrial soil organic C stocks has generated interest in clarifying the processes that underlie soil C dynamics. We investigated the role of soil mineralogy and aggregate stability as key variables controlling soil C dynamics in a California conifer forest. We characterized soils derived from granite (GR) and mixed andesite‐granite (AN) parent materials from similar forest conditions. Granite and AN soils contained similar clay mineral assemblages as determined by x‐ray diffraction (XRD), dominated by vermiculite, hydroxy‐interlayered vermiculite (HIV), kaolinite, and gibbsite. However, AN soils contained significantly more Al in Al‐humus complexes (6.2 vs. 3.3 kg m−2) and more crystalline and short‐range order (SRO) Fe oxyhydroxides (30.6 vs. 16.8 kg m−2) than GR soils. Andesite‐granite pedons contained nearly 50% more C relative to GR soils (22.8 vs. 15.0 kg m−2). Distribution of C within density and aggregate fractions (free, occluded, and mineral associated C) varied significantly between AN and GR soils. In particular, AN soils had at least twice as much mineral associated C relative to GR soils in all horizons. Based on 14C measurements, occluded C mean residence time (MRT) > mineral C > free C in both soil types, suggesting a significant role for aggregate C protection in controlling soil C turnover. We found highly significant, positive correlations between Al‐humus complexes, SRO Al minerals, and total C content. We suggest that a combination of aggregate protection and organomineral association with Al‐humus complexes and SRO Al minerals control the variation in soil C dynamics in these systems.
A Priori v Post Hoc Testing
Authors: Berger Vance W.
Published: September 2005

In many situations, statistical analyses are given more credibility when they are planned prospectively, that is, in advance of the collection of the data. Such prespecified analyses cannot be said to have been suggested by the data, as post hoc analyses would be. A simple measure to prevent the difficult interpretation that can arise from post hoc analyses is to simply avoid them. This approach may be overly conservative, however, as the need may arise to test a hypothesis that the data suggested, and use the same data to test it. Several approaches exist for doing so, although not all of them have been cast as tests of post hoc analyses. In the analysis of variance framework, Fisher's least significant difference (LSD) and Scheffe's method can both be viewed as a means of performing post hoc linear comparisons, although they may not always be explicitly referred to as post hoc analyses. The Smirnov test may be viewed as a method to allow for the data to select the cut point of an ordinal outcome measure. Certain adaptive tests for ordinal data may be viewed as taking the Smirnov test to the next level, and allowing for the optimal combination of cut points, based on the data. The information‐preserving composite end point is a method to allow for data‐dependent outcome measure selection.
Additive Constant Problem
Authors: Trosset Michael W.
Published: September 2005

The additive constant problem arises in multidimensional scaling (MDS), a collection of techniques for visualizing dissimilarity data. Certain experiments result in dissimilarities that are most naturally modeled as distances from which an unknown constant has been subtracted. The additive constant problem is the problem of estimating this unknown constant.
Additive Genetic Variance
Authors: Posthuma Danielle
Published: September 2005

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.
Age–Period–Cohort Analysis
Authors: Holford Theodore
Published: September 2005

When evaluating temporal trends, three perspectives to be considered are age, period and cohort. Age refers to chronological age of a subject when the outcome of interest occurs, and period the corresponding calendar date. Cohort refers to the year of birth, which identifies a generation, but this may be calculated by taking the difference between age and period, which can make these analyses difficult to interpret. Approaches for analyzing these temporal perspectives are described, including approaches to the identifiability problem that arises when one attempts to fit models that include all three temporal parameters at the same time. These methods are demonstrated using data on births per 1000 women among US blacks.
All‐
X
Models
Authors: Landis Ronald S.
Published: September 2005

All‐X models refer to applications of confirmatory factor analytic procedures in which the researcher treats all latent variables as exogenous. Model specification requires estimation of three sets of parameters including (a) loadings of measured variables on the underlying construct(s), (b) measurement errors associated with the observed variables, and (c) relationships, if any, between the latent exogenous constructs.
Allelic Association
Authors: Cardon Lon R.
Published: September 2005

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.
Alternating Treatments Design
Authors: Herrera Gina Coffee , Kratochwill Thomas R.
Published: September 2005

The alternating treatments design (ATD) is a type of single‐participant design that is commonly used to determine the effectiveness of two or more treatments. Conditions in the ATD are alternated rapidly and randomly/semirandomly, and effects can by analyzed statistically (e.g., randomization tests) and/or by visual inspection. Using the ATD can be advantageous because treatment effects can be found rather quickly and a withdrawal phase and baseline data are not required.
Analysis of Covariance
Authors: Huitema Bradley E.
Published: September 2005

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.
Incomplete Contingency Tables
Authors: Hershberger Scott L.
Published: September 2005

Incomplete contingency tables have cells with no observations. When empty cells are predicted on the basis of theory or characteristics of the population, the cells have structural zeros. When empty cells occur from deficiencies in sampling, the cells have sampling zeros. While the analytic consequences of structural zeros are usually benign, the consequences arising from sampling zeros are often not so. The primary problem is deterioration in the accuracy of theG2and χ2approximations for testing the fit of loglinear models. Under these conditions, various strategies can be used to improve model testing.
Analysis of Variance
Authors: Serlin Ronald C.
Published: September 2005

The analysis of variance unites seemingly unrelated inferential methods into one exact procedure whose results can be reported in a single compact table. The analysis consists of partitioning the overall variability present in the data into parts reflecting the variability accounted for by explanatory factors and variability due to chance. If the amount of variability accounted for by an explanatory factor is enough larger than we would expect it to be simply due to the vagaries of random sampling, we conclude that there is a nonzero effect in the population due to the factor under examination. The analysis of variance allows a researcher to employ this logic while limiting the rate at which erroneous conclusions are drawn.
Analysis of Variance: Multiple Regression Approaches
Authors: Harris Richard J.
Published: September 2005

One‐way and factorial independent‐means analysis of variance (ANOVA) can be accomplished via a multiple regression analysis (MRA) in which the dependent variable is predicted from sets of level‐membership variables or, preferably, contrast variables representing the factors. Doing so can be useful in the two‐group design in that it provides an easily interpretable and often remarkably low estimate of the proportion of variance in the dependent variable, that is accounted for by an experimental manipulation. It also helps in the choice among alternative approaches to analyzing unequal‐
n
factorial designs by showing that full‐model analyses involve the comparison of unweighted means and that testing an effect uncorrected for any other effects implies making comparisons among weighted means. Furthermore, testing an effect corrected for confounds with some, but not all, other effects involves comparisons among the means that are difficult to characterize, require hand computations beyond those provided by ANOVA computer programs, and are unlikely to be of any real interest to the researcher. SPSS commands for carrying out factorial ANOVA via the MANOVA and REGRESSION commands are provided.
A
nsari–
B
radley Test
Authors: Lunneborg Clifford E.
Published: September 2005

The Ansari–Bradley procedure provides a distribution‐free test of the equivalence of the variances in two distributions having a common median.
Area Sampling
Authors: Welkenhuysen‐Gybels Jerry , Heerwegh Dirk
Published: September 2005

Area sampling is a sampling technique that uses fractions of the earth's surface as primary sampling units. Usage of this sampling technique in behavioral research is discussed.
Ascertainment Corrections
Authors: Neale Michael C.
Published: September 2005

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.
Model Fit: Assessment of
Authors: Glas Cees A. W.
Published: September 2005

The fit of item response theory models can be evaluated by the computation of residuals and the associated test statistics. The procedures are targeted at three assumptions: the form of the item response curves, the assumption of local stochastic independence and the assumption of subpopulation invariance. A general statistical framework is given for the definition of test statistics with a know asymptotic distribution. The procedures are worked out in some detail for the 3‐parameter logistic model and the generalized partial credit model. Further, it is outlined how the procedures generalize to other parametric models for dichotomously and polytomously scored items. An example from a school effectiveness study serves as a small illustration. Finally, some references to alternative approaches to the evaluation of model if are given.
Test Dimensionality: Assessment of
Authors: Gessaroli Marc E. , de Champlain Andre F.
Published: September 2005

The ability to properly assess the dimensionality of a set of item responses is essential in psychometrics. Many popular psychometric methods using item response theory (IRT) such as item calibration and test equating assume that there is a single latent trait underlying the responses. As well, the popularity of multidimensional models has increased. Furthermore, test dimensionality is directly linked to the structural aspect of the construct validity of a test. History has shown some confusion in the definition of dimensionality and in the methods to assess it. Recent literature relates both the definition of dimensionality and its methods of assessment to the concepts of local independence and essential independence. This entry provides an overview of these definitions and of the more current methods of dimensionality assessment.
Assortative Mating
Authors: Hershberger Scott L.
Published: September 2005

Assortative mating refers to nonrandom mating between individuals, and it can be based on observable characteristics of individuals, or characteristics of their environments, or both. Positive assortative mating is by far more common than negative assortative mating. If positive assortative mating is based on similarity of observable characteristics of individuals, the population genetic variance of the characteristic increases in the offspring generation. On the other hand, if positive assortative mating is based solely on environmental characteristics, genetic variance is unaffected. It is unlikely, however, that either mechanism of mate selection occurs in isolation for any single characteristic.
Asymptotic Relative Efficiency
Authors: Lunneborg Clifford E.
Published: September 2005

Two competing test statistics may differ in their sensitivity to deviations from the null hypothesis. A standard comparison is the asymptotic relative efficiency (ARE) – the limit, as sample sizes grow, of the ratio of the sample sizes needed for the two tests to attain the same power.
Attitude Scaling
Authors: Johnson Matthew S. , Junker Brian W.
Published: September 2005

Attitude scaling is the process of locating
J
attitude statements or items and/or
N
individuals on a latent continuum or scale. There are numerous methods available for the scaling of attitudes. The earliest methods, developed by L. L. Thurstone, R. Likert, and L. Guttman required two stages: One stage to locate the items on the scale and a second stage to locate the respondents. Other early work on the scaling of attitudes by C. H. Coombs allowed for the simultaneous estimation of both items and respondents, but the assumptions proved too restrictive for most real examples. Modern attitude scaling techniques use item response theory models, both monotone and unfolding, to perform attitude scaling.
Attrition
Authors: Shadish William R. , Luellen Jason K.
Published: September 2005

Attrition refers to a loss of response from study participants. Random attrition reduces statistical power but does not bias the results of a study. Systematic attrition reduces statistical power and can lead to generalizability biases and causal inference biases. For these reasons, it is best to prevent or minimize attrition. Attrition from measurement can and should be avoided even when attrition from treatment cannot be avoided. For attrition that cannot be prevented, a number of alternative strategies attempt to account for attrition during data analysis.
Free Response Data Scoring
Authors: Clauser Brian E. , Margolis Melissa J.
Published: September 2005

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.
Test Construction: Automated
Authors: Veldkamp Bernard P.
Published: September 2005

Optimal test construction deals with automated assembly of tests for educational and psychological measurement. Items are selected from an item bank to meet a predefined set of test specifications. Several models for optimal test construction are presented, and two algorithms for optimal test assembly are discussed. A number of current issues are mentioned.
Axes in Multivariate Analysis
Authors: Krzanowski Wojtek J.
Published: September 2005

Quantitative multivariate data can be represented geometrically as points in space, where the measured variables correspond to coordinate axes. Dimensionality reduction is effected by projecting the points into a subspace of this data space. Different projections highlight different features of the data. Some techniques produce linear combinations of the variables, and these linear combinations form the axes of the resultant subspace, while other techniques either find the subspace directly or implicitly. Consequences of these different possibilities are briefly discussed.
Bagging
Authors: Cutler Adele , Corcoran Chris , Toone Leslie
Published: September 2005

Bagging stands for ‘
b
ootstrap
ag
gregating’ and means that we apply a regression or classification procedure to bootstrap samples from the original data and aggregate the results. A ‘bagged’ procedure is often much more accurate than the original, although it is slower and more difficult to interpret.
Balanced Incomplete Block Designs
Authors: Cotton John W.
Published: September 2005

Balanced incomplete block designs (BIBD) are experimental designs that can be described by a labeled, fully filled rectangular array or by a larger equivalent rectangular display with some empty cells. A cell in the rectangle identifies one treatment in a given block. The number of different treatments for a given block (individual) is less than the total number of treatments; this precludes having each treatment appear once in every block. Hence, the design is anincompleteblock design. Without some constraints on the number of occurrences of certain treatments in different rows, the average performance in different parts of the design can be biased. This bias yields different averages for two treatments, for example, because of differences in the cells in which those treatments were employed. To eliminate the bias, a special form of balance is employed in which every possible pair of treatments appears in an equal number of blocks. Accordingly, there also is balance in the total number of times each treatment occurs. Analysis methods are discussed for general linear fixed, mixed, and Bayesian models. I discuss a special case of a BIBD design, Youden squares, that permits but does not require the assessment of one more independent variable than other BIBDs. In the behavioral sciences, the rows of Youden squares can be different blocks, typically different people or animals, and the columns can be different observation occasions that permit the assessment of so‐called period effects.
Bar Chart
Authors: Everitt Brian S.
Published: September 2005

A bar chart is graphical display of the observed frequencies associated with each category of a categorical variable. The height of a rectangular bar drawn over each category is proportional to the frequency.
Item Response Theory (IRT) Models for Dichotomous Data
Authors: Hambleton Ronald K. , Zhao Yue
Published: September 2005

This entry provides an introduction to the topic of item response theory. Shortcomings of classical test models are considered first. Second, current item response models for the analysis of dichotomously scored item response data are introduced. Estimation of model parameters, assessment of model fit, and available software, are described next. Finally, applications of item response theory IRT models to test development, item bias, equating, and computer‐adaptive testing are briefly described.
Battery Reduction
Authors: Massaro Joseph M.
Published: September 2005

Battery reduction is used to select a subset of
m
variables from an original set of
n
variables (
m
 < 
n
) that reproduce a large proportion of the variance in the original set of
n
variables. There are a number of procedures for performing battery reduction analysis. A popular method involves performing a principal components analysis first to select
m
components, which account for the salient variance in the original data. Gram–Schmidt orthogonal rotations are then performed to determine the
m
variables that account for the largest proportion of variance. The procedure is illustrated and reference made to a SAS macro for performing the analysis.
Bayesian Item Response Theory Estimation
Authors: Swaminathan Hariharan
Published: September 2005

The problem of fundamental importance in item response theory is the accurate estimation of the parameters that characterize the items and the ability or the proficiency levels of examinees. While in general, likelihood‐based approaches such as joint, conditional, and marginal maximum likelihood procedures provide reasonable estimates of the item parameters, more often than not these procedures yield estimates in the two‐ and three‐parameter models that are inadmissible in the sense that the estimates of the ability and item parameters fall outside acceptable ranges. Bayesian approaches, by taking into account prior and collateral information, overcome the problems encountered by likelihood‐based approaches.
Bayesian Methods for Categorical Data
Authors: Gutiérrez‐Peña Eduardo
Published: September 2005

The Bayesian approach to statistical inference is based on a subjective interpretation of probability. Given a prior distribution describing our prior beliefs concerning the value of an unknown quantity, Bayes' theorem allows us to update those beliefs in the light of observed data. The resulting posterior distribution then summarizes all the available information about the quantity of interest, conditional on the posited model. Bayesian methods for the analysis of categorical data use the same classes of models as the classical approach. However, Bayesian analyses can be more informative – the posterior distribution of the parameter contains more information than mere point estimates or test statistics – and may provide more natural solutions in certain situations such as those involving sparse or missing data, or unidentifiable parameters.
Bayesian Statistics
Authors: Phillips Lawrence D.
Published: September 2005

Bayesian statistics is based on the interpretation of a probability as a degree of belief held by an individual about some hypothesis, event, or uncertain quantity. These ‘prior’ degrees of belief are revised in light of new information by the application of Bayes's theorem, a simple consequence of the addition and multiplication laws of probability, thereby resulting in a ‘posterior’ probability distribution. Bayesian statistical inferences are based on this posterior distribution, usually in the form of probability statements about the hypotheses, events, or uncertain quantities of interest, sometimes giving different inferences from classical statistical methods even though they are both based on the same data.
Binomial Effect Size Display
Authors: Rosenthal Robert
Published: September 2005

The binomial effect size display (BESD) was designed to indicate the practical importance of any particular effect size estimate. The BESD displays the difference between two proportions such as the difference between the success rate of a new intervention and the success rate of the standard intervention. For example, an
r
of 0.20 is said to account for ‘only 4% of the variance’, but the BESD shows that this proportion of variance accounted for is equivalent to increasing the success rate (e.g., survival rate, improvement rate, etc.) from 40 to 60%.
Biplot
Authors: Gower John
Published: September 2005

Biplots give graphical displays of approximations to the values of two‐way arrays. Variants allow for multivariate data matrices, two‐way tables, correlation, covariance, and (dis)similarity matrices and for quantitative, nominal, and ordered categorical variables. Plotting devices include points to represent cases or samples, calibrated axes, possibly curvilinear, to represent quantitative variables, and convex regions to represent categorical variables.
Block Random Assignment
Authors: Berger Vance W.
Published: September 2005

The validity of the comparison between any two treatment groups is predicated on the comparability of these two treatment groups in all relevant respects other than the treatments under study. Without this condition, any observed differences in the outcomes across the treatment groups can be attributed to either the treatments or the underlying differences having nothing to do with the treatments. To definitively attribute observed differences to the treatments themselves (the intended conclusion), all competing potential attributions must be falsified. Randomization is used to create comparable comparison groups, but the extent to which it does so depends on the specifics of how it is implemented. If at some point during the study there exists an imbalance across treatment groups in the number of subjects allocated, and this imbalance is subsequently corrected, then time effects are confounded with treatment effects, and the comparison groups may not be comparable. To ensure that this chronological bias does not occur, block randomized assignment may be used. This ensures that at no point in time will the imbalance ever become larger than some specified threshold. One drawback to block randomized assignment is that it can lead to predictable allocations. If the allocation sequence is predictable, then selection bias could interfere with the comparability of the comparison groups. The trade‐off between chronological bias and selection bias has implications for the selection of the block size, or the use of alternative procedures.
Boosting
Authors: Cutler Adele
Published: September 2005

Boosting is a method that attempts to increase the prediction accuracy of a classification or regression method by repeatedly re‐weighting the data and combining the results. The boosted classifier generally had good accuracy, but it is not readily interpretable.
Bootstrap Inference
Authors: Canty A. J. , Davison Anthony C.
Published: September 2005

Bootstrap methods are powerful and widely useful simulation procedures, whose applications include assessing the uncertainty of a statistical estimator, constructing a confidence interval, and computing the significance probability associated with a hypothesis test.
Box Plots
Authors: Lovie Sandy
Published: September 2005

The box plot is one of the most popular of the graphical displays developed by the exploratory data analysis (EDA) movement. It is especially useful for summarizing the main distributional features of data, such as location, spread, and shape, as well as for flagging up potential outliers.
Intervention Analysis
Authors: Brockwell P. J.
Published: September 2005

An outline is given of the intervention model of Box and Tiao and the way it can be applied to the modeling of changes in level of a time series due to interventions at specified times during the period of observation of the series. An example illustrating the effect of seat‐belt legislation on deaths and serious injuries on UK roads is included.
Bradley–Terry Model
Authors: Marden John I.
Published: September 2005

In a paired comparison experiment, several judges compare several objects. Each judge performs a number of paired comparisons, that is, considers a pair of the objects, and indicates which of the two is preferred. The Bradley–Terry model is a simple but powerful model in which each object is equipped with a measure of overall strength, and the chance that one object is preferred to another depends on only their relative strengths. The model is useful not only for preference data but also for competitions, for example, among sports teams.
Breslow–Day Statistic
Authors: Wang Molin , Berger Vance W.
Published: September 2005

When analyzing a series of stratified 2 × 2 tables, the analysis can be simplified if each table has a common odds ratio. The Breslow–Day‐statistic is a test statistic for testing this homogeneity against the global alternative of heterogeneity.
Bubble Plot
Authors: Everitt Brian S.
Published: September 2005

A bubble plot is a way of representing the relationship between three variables on a scatterplot. Observations on two variables are plotted in the usual way using circles as symbols; the radii of the circles are made proportional to the associated values for the third variable.
Canonical Correlation Analysis
Authors: Thompson Bruce
Published: September 2005

Canonical correlation analysis is a statistical method employed to investigate relationships among two or more variable sets, each consisting of at least two variables. Canonical correlation analysis is the multivariate form of the general linear model, which presumes that all analyses are correlational, derive estimates by applying weights to measured variables, and yield variance‐accounted‐for effect sizes. As is the case throughout the GLM, noteworthy functions must be interpreted by consulting both standardized weights and structure coefficients in concert with each other.
Carroll–Arabie Taxonomy
Authors: de Leeuw Jan
Published: September 2005

The Carroll–Arabie taxonomy of scaling methods gives a way to systematically describe data and models used in the computerized geometrical approach to scaling developed in psychometrics since 1960.
Carryover and Sequence Effects
Authors: Putt Mary E.
Published: September 2005

Carryover and sequence effects arise when repeated measurements are collected over time on experimental subjects. We illustrate how these nuisance parameters potentially bias estimates of the treatment effect, and discuss trade‐offs between bias and variance in two split‐plot factorial designs.
Case Studies
Authors: Onghena Patrick
Published: September 2005

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.
Case–Cohort Studies
Authors: Langholz Bryan
Published: September 2005

The case‐cohort study method is a sampling technique used in epidemiologic research of rare diseases. In this article, the design and its use in medical research is described. Two real examples are provided and a discussion of computer software to analyze case‐cohort data is included.
Case–Control Studies
Authors: Berger Vance W. , Zhang Jialu
Published: September 2005

A case–control study is an observational study that is generally retrospective in nature. In case–control studies, investigators sample subjects with and without some trait (possibly a disease), and then look back in time to assess the antecedent risk factors. In the case–control study, the margin of disease is fixed. This means that we cannot directly calculate the relative risk. However, when the prevalence of the disease is low, the relative risk can be approximated by the odds ratio, which can always be computed regardless of whether the study is retrospective or prospective.
Catalogue of Probability Density Functions
Authors: Everitt Brian S.
Published: September 2005

Probability density functions are the foundations of almost all statistical methodology. Here we give a brief account of the density functions most often encountered in practice along with a summary of their most important properties.
Catalogue of Parametric Tests
Authors: Clark‐Carter David
Published: September 2005

Parametric tests derive their probability from probability distributions such as the normal distribution and thetdistributions. They make more assumptions about the nature of the population from which samples have been drawn than their nonparametric equivalents. However, when the assumptions of a parametric test are met, the test will have more statistical power than a nonparametric equivalent.
Catastrophe Theory
Authors: Wagenmakers E.‐J. , van der Maas H. L. J. , Molenaar P. C. M.
Published: September 2005

Catastrophe theory describes how small, continuous changes in control parameters (i.e., independent variables that influence the state of a system) can have sudden, discontinuous effects on dependent variables. Such discontinuous, jumplike changes are called
phase‐transitions
or
catastrophes
. Examples include the sudden collapse of a bridge under slowly mounting pressure, and the freezing of water when temperature is gradually decreased. This entry expands on the ideas inherent in catastrophe models.
Categorizing Data
Authors: Durkalski Valerie , Berger Vance W.
Published: September 2005

In epidemiology and clinical studies, it is common to categorize continuous outcomes and/or prognostic variables with the goal of simplifying both the interpretation of results and the statistical methods. However, the implications of categorization and the trade‐off between statistical power and simplicity should be understood before taking on this approach, because information, and hence power, can be lost in the process.
Analysis of Variance: Cell Means Approach
Authors: Kirk Roger E. , Bekele B. Nebiyou
Published: September 2005

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.
Censored Observations
Authors: Berger Vance W.
Published: September 2005

Observations are referred to ascensoredwhen the variable of interest is not completely observed but some information on the value is available. For example, in oncology clinical trials, one is interested in patients' survival time, measured as the time from entry (or randomization) until death. But owing to budget and other practical constraints, trials cannot be run until all patients have experienced the event (be it death or some other endpoint). The ‘time to death’ is not observed for any patient who is still alive at the end of the trial, but it would not be accurate to state that no information is available. It is known that their time to death is at least as long as the time from enrollment until the end of the observation period of the trial. We call the observation censored or, more precisely, right censored. Observations can also be left censored, for example when an assay has a detection limit. It is also possible for an observation to be interval censored, such as when repeated screening for breast cancer reveals that cancer developed between two consecutive screenings. Special methods of analysis are required for censored data, to account for what is known and what is not known.
Census
Authors: Welkenhuysen‐Gybels Jerry , Heerwegh Dirk
Published: September 2005

The current contribution provides a definition of a population census, describes what its goals are, and gives a short history of the census. The second part of the contribution focuses on methodological issues related to the conduct of the census and the collection of the census information.
Centering in Linear Multilevel Models
Authors: de Leeuw Jan
Published: September 2005

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.
Central Limit Theory
Authors: Miles Jeremy
Published: September 2005

The central limit theorem underlies a wide range of statistical tests – it is one of the most important theorems in statistics. It states that the sampling distribution of the mean will approach normal, as the sample size increases, whatever the shape of the distribution in the population. It is easy to demonstrate by considering the distribution of dice rolls. There are occasions when the central limit theorem does not apply, for example, when the distribution of the measurement is made up of a mixture of distributions.