Distinguishing between moderator and quadratic effects in multiple regression.

Moderated regression analysis is commonly used to test for multiplicative influences of independent variables in regression models. D. Lubinski and L. G. Humphreys (1990) have shown that significant moderator effects can exist even when stronger quadratic effects are present. They recommend comparing effect sizes associated with both effect types and selecting the model that yields the strongest effect. The authors show that this procedure of comparing effect sizes is biased in favor of the moderated model when multicollinearity is high because of the differential reliability of the quadratic and multiplicative terms in the regression models. Fortunately, levels of multicollinearity under which this bias is most problematic may be outside the range encountered in many empirical studies. The authors discuss causes and implications of this phenomenon as well as alternative procedures for evaluating structural relationships among variables. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multivariate analysis of variance by multiple regression methods.

Discusses multivariate analysis of variance as a general case of familiar multiple regression analysis. A consequence of this approach is a unified treatment of multivariate analysis of variance which can be used by psychologists who are generally familiar with multiple regression approaches to univariate analysis of variance. It is suggested that the generality of the approach permits solutions consistent with any of the several available strategies for dealing with problems of unequal and disproportionate cell frequencies. Inherent in the multiple regression formulation is the otherwise not so obvious fact that univariate analysis of variance results are an integral part of the multivariate solution and that both are important for understanding complex data. Methods of interpreting multivariate analysis of variance results in complex factorial experimental designs are discussed. (32 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Methodological artifacts in moderated multiple regression and their effects on statistical power.

Monte Carlo simulations were conducted to examine the degree to which the statistical power of moderated multiple regression (MMR) to detect the effects of a dichotomous moderator variable was affected by the main and interactive effects of (a) predictor variable range restriction, (b) total sample size, (c) sample sizes for 2 moderator variable-based subgroups, (d) predictor variable intercorrelation, and (e) magnitude of the moderating effect. Results showed that the main and interactive influences of these variables may have profound effects on power. Thus, future attempts to detect moderating effects with MMR should consider the power implications of both the main and interactive effects of the variables assessed in the present study. Otherwise, even moderating effects of substantial magnitude may go undetected. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling two-state disease processes with random effects

Many chronic medical conditions are manifested by alternating sojourns in symptom-free and symptomatic states. In many cases, in addition to their relapsing and remitting nature, these conditions lead to worsening disease patterns over time and may exhibit seasonal trends. We develop a mixed-effect two-state model for such disease processes in which covariate effects are modeled multiplicatively on transition intensities. The transition intensities, in turn, are functions of three time scales: the semi-Markov scale involving the backward recurrence time for the cyclical component, the Markov scale for the time trend component, and a seasonal time scale. Multiplicative bivariate log-normal random effects are introduced to accommodate heterogeneity in disease activity between subjects and to admit a possible negative correlation between the transition intensities. Maximum likelihood estimation is carried out using Gauss-Hermite integration and a standard Newton-Raphson procedure. Tests of homogeneity are presented based on score statistics. An application of the methodology to data from a multi-center clinical trial of chronic bronchitis is provided for illustrative purposes.     

Fitting Weibull duration models with random effects

The interstitial space of the thyroid gland of adult marmosets contains, like the stroma of other organs, cells and intercellular substance (matrix), blood vessels (predominantly capillaries), lymph vessels and unmyelinated nerves. It is demarcated from the follicular epithelium, the capillaries and Schwann cells by a basal lamina (BL). The perifollicular BL shows thickenings of up to 3 microns over long distances or a multilayered arrangement. These thickened segments exhibit numerous epithelial processes and ridges; in other words, the contour of the basal cell membrane is very irregular in these areas. Indentations of capillaries into the epithelium are rarely observed. The endothelium is only slightly porous. Lymph capillaries occur in large numbers. They originate freely in the interstitial space, show gaps or unspecific contacts between the thin endothelial cells; a basal lamina is missing. Bundles of 10-nm thick filaments (anchor filaments) extend to the endothelial cells of the lymph capillaries. Thin and very long (up to 8 microns) plate-like processes surround the capillaries or run parallel to the outer contour of the follicles. They originate at the poles of oval, fibroblast-like cells. Since these cells are FXIII- and C3bi-positive, they can be considered as dendritic cells. They obviously play a role in the frequently-observed autoimmune diseases of this species. In addition, monocytes and all transitional forms including macrophages, fibrocytes and lymphocytes as well as numerous mast cells occur. In the region of the BL, integrins of the beta 1-group (alpha 6) can be demonstrated immunohistologically in addition to the usual components (collagen type IV, laminin and heparan sulfate-proteoglycan). Of the fibrillar collagens type I does not occur, type III occurs only in small amounts, whereas types V and VI are observed in large amounts. The presented findings may serve as basis for more extensive experiments on these primates.     

Failures to detect moderating effects with ordinary least squares-moderated multiple regression: Some reasons and a remedy.

[Correction Notice: An erratum for this article was reported in Vol 100(2) of Psychological Bulletin (see record 2008-10954-001). Several errors went uncorrected. On page 283, the second line of the first full paragraph should read "in Equation 3...." On page 284, in the eighth line of the first full paragraph, the power in the equation should be "1/2," not "12." On page 287, in Table 4, the heading for column 6 should read "Adjusted SS for deletion of X?X?," not just "X?." The heading for column 7 should read "H?: β?=0c, partial F," not "β?." Finally, in line 3 of the table note, "X?X?" should read "X?,X?."] Describes a means for determining circumstances when ordinary least squares/moderated multiple regression (OLS/MMR) may be at risk in moderator applications and suggests an alternative regression procedure that can be used to overcome the threat of Type II error posed by these circumstances. Using field study data on job satisfaction of employees at state institutions for the developmentally disabled, parallel analyses are presented to show how markedly different results can be obtained between OLS/MMR and the remedial procedure. It is suggested that investigators who have reached conclusions with the traditional OLS/MMR approach reexamine their data. (23 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Meta-analysis of physiological outcomes of hospital-based infant intervention programs

A meta-analysis of 37 studies on the effects of an intervention program on physiological development of the premature infant was conducted using multidisciplinary research reported since 1979. Two homogeneous populations based on mean infant birth weight were found in which infant stimulation programs were shown to be effective in enhancing physiological development (Group I: mean birth weight = 1,260 g, mean combined effect size = 1.54, SD = 0.54, 95% confidence interval = 1.22-1.86; Group II: mean birth weight = 1,524 g, mean combined ES = 0.51, SD = 0.30, 95% confidence interval = 0.39-0.63). The robustness of Group I was tentative based on the lack of small sample-small effect nonsignificant result studies discovered in the funnel plot. Group II results (based upon 1,306 infants) are a stronger indication that, in general, infants with birth weights greater than 1,500 g are positively affected by the initiation of a stimulation program during hospitalization.     

A likelihood approach to meta-analysis with random effects

In a meta-analysis of a set of clinical trials, a crucial but problematic component is providing an estimate and confidence interval for the overall treatment effect theta. Since in the presence of heterogeneity a fixed effect approach yields an artificially narrow confidence interval for theta, the random effects method of DerSimonian and Laird, which incorporates a moment estimator of the between-trial components of variance sigma B2, has been advocated. With the additional distributional assumptions of normality, a confidence interval for theta may be obtained. However, this method does not provide a confidence interval for sigma B2, nor a confidence interval for theta which takes account of the fact that sigma B2 has to be estimated from the data. We show how a likelihood based method can be used to overcome these problems, and use profile likelihoods to construct likelihood based confidence intervals. This approach yields an appropriately widened confidence interval compared with the standard random effects method. Examples of application to a published meta-analysis and a multicentre clinical trial are discussed. It is concluded that likelihood based methods are preferred to the standard method in undertaking random effects meta-analysis when the value of sigma B2 has an important effect on the overall estimated treatment effect.     

Kinetic parameter estimation by numerical algorithms and multiple linear regression: theoretical

A new method is presented for the determination of kinetic parameters based on a functional relationship among experimental data derived from the postulated model. The data, even though containing errors, are manifestations of this relationship, which should be satisfied by parameters fitted to the system. The procedure involves the use of numerical integration and/or differentiation of the data, followed by multiple linear regression. It does not require initial estimates or repetitive iteration for linear systems and can be applied to nonlinear models. The accuracy of estimated parameter depends on the goodness of the particular numerical approximation method used.     

Sample size and multiple regression analysis.

Despite the development of procedures for calculating sample size as a function of relevant effect size parameters, rules of thumb tend to persist in designs of multiple regression studies. One explanation for their persistence may be the difficulty in formulating a reasonable a priori value of an effect size to be detected. This article presents methods for calculating effect sizes in multiple regression from a variety of perspectives and also introduces a new method based on an exchangeability structure among predictor variables. No single method is deemed superior, but rather examples show that a combination of methods is likely to be most valuable in many situations. A simulation provides a 2nd explanation for why rules of thumb for choosing sample size have persisted but also shows that the outcome of such underpowered studies will be a literature consisting of seemingly contradictory results. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于支持向量机和随机森林算法结合光纤式激光诱导击穿光谱定量检测核电用钢中铬

核电站关键设备的状态检测技术是核电安全性和经济性的重要保障,激光诱导击穿光谱技术(Laser-induced breakdown spectroscopy,LIBS)是一种可在线测量材料元素组分及其结构状态的新方法,基于采用光纤传输激光并回传等离子体辐射的光纤式激光诱导击穿光谱(fiber-optic LIBS,FO-...     

Norming clinical questionnaires with multiple regression: The Pain Cognition List.

Questionnaires for measuring patients' feelings or beliefs are commonly used in clinical settings for diagnostic purposes, clinical decision making, or treatment evaluation. Raw scores of a patient can be evaluated by comparing them with norms based on a reference population. Using the Pain Cognition List (PCL-2003) as an example, this article shows how clinical questionnaires can be normed with multiple regression of raw scores on demographic and other patient variables. Compared with traditional norm tables for subgroups based on age or gender, this approach offers 2 advantages. First, multiple regression allows determination of which patient variables are relevant to the norming and which are not (validity). Second, by using information from the entire sample, multiple regression leads to continuous and more stable norms for any subgroup defined in terms of prognostic variables (reliability). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Relative power of moderated multiple regression and the comparison of subgroup correlation coefficients for detecting moderating effects.

A Monte Carlo simulation assessed the relative power of 2 techniques that are commonly used to test for moderating effects. 500 samples were drawn from simulation-based populations for each of 81 conditions in a design that varied sample size, the reliabilities of 2 predictor variables (1 of which was the moderator variable), and the magnitude of the moderating effect. The null hypothesis of no interaction effect was tested by using moderated multiple regression (MMR). Each sample was then successively polychotomized into 2, 3, 4, 6, and 8 subgroups, and the equality of the subgroup-based correlation coefficients (SCC) was tested. Results show MMR to be more powerful than the SCC strategy for virtually all of the 81 conditions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Distortions of construct validity in multiple regression analysis.

Contends that interpreting the results of multiple regression analyses in terms of the constructs reflected by variables originally entered into the analyses is hazardous because the statistics of interest produced by the analysis (e.g., regression coefficients) are computed using residualized scores. These scores reflect constructs that differ from those described by the original raw scores. Interpretive hazards are demonstrated with a simple fabricated set of data and then with actual data from a study by the author and R. W. Marx (1981) relating 118 schoolchildren's self-concept to their achievement. It is recommended that anchor variables not of direct interest in a research study be measured and correlated with residualized variables. This supplementary analysis sheds light on changes to construct validity that must be known before interpreting multiple regression analyses. (French abstract) (19 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparison of hot rolled steel mechanical property prediction models using linear multiple regression,non-linear multiple regression and non-linear artificial neural networks

Abstract

In the manufacture of rolled steel from a hot strip mill, the final mechanical properties, such as yield strength, ultimate tensile strength and elongation to fracture, are important requirements specified by the customer. The use of mathematical modelling techniques such as multiple regression analysis, or computational developments such as artificial neural networks, can result in the creation of acceptably accurate predictive models. However, the accuracy of any predictive model will depend on the quality of data used in its creation, and thus a brief statistical analysis of the mechanical property data used for model development is discussed. In the present paper a comparison of the application of linear multiple regression, non-linear multiple regression and non-linear neural networks is made for various steel families using data taken from the Corus Port Talbot hot strip mill. A statistical summary of their relative predictive errors is given, and although all three are comparable, the non-linear, black box approach of a suitably structured neural network provides overall more accurate predictive models than the use of linear or non-linear multiple regression.     

Distribution of fitness effects caused by random insertion mutations in Escherichia coli

Very little is known about the distribution of mutational effects on organismal fitness, despite the fundamental importance of this information for the study of evolution. This lack of information reflects the fact that it is generally difficult to quantify the dynamic effects of mutation and natural selection using only static distributions of allele frequencies. In this study, we took a direct approach to measuring the effects of mutations on fitness. We used transposon-mutagenesis to create 226 mutant clones of Escherichia coli. Each mutant clone carried a single random insertion of a derivative of Tn10. All 226 mutants were independently derived from the same progenitor clone, which was obtained from a population that had evolved in a constant laboratory environment for 10,000 generations. We then performed competition experiments to measure the effect of each mutation on fitness relative to a common competitor. At least 80% of the mutations had a significant negative effect on fitness, whereas none of the mutations had a significant positive effect. The mutations reduced fitness by about 3%, on average, but the distribution of fitness effects was highly skewed and had a long, flat tail. A compound distribution, which includes both gamma and uniform components, provided an excellent fit to the observed fitness values.     

A confidence interval for ordinal multiple regression weights.

A confidence interval (CI) for a population predictor weight for use with N. Cliff's (1994) method of ordinal multiple regression (OMR) is presented. The OMR CI is based on an estimated standard error of a weight derived from a fixed-effects model. A simulation was performed to examine the sampling properties of the OMR CI. The results show that the OMR CI had good Type I error rate and coverage. The OMR CI had lower power than the least-squares multiple regression (LSMR) CI when predictors were not correlated but had higher power when predictor correlations were moderate to high. In addition to discussing the simulation results, it is pointed out that the OMR CI can have superior sampling properties when the fixed-effects assumptions are violated. The OMR CI is recommended when a researcher wants to consider only ordinal information in multivariate prediction, when predictor correlations are moderate to high, and when the assumptions of fixed-effects LSMR are violated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)