Parametric statistics and levels of measurement.

If Y is a continuous, ordinal measure of latent variable θ and Y has a normal distribution with equal variances in several groups, then t tests and one-way analyses of variance on Y can be used to test hypotheses about population mean differences on θ in the corresponding groups. If X and Y are continuous, ordinal measures of latent variables θ and φ, and if X and Y have a bivariate normal distribution, then a test of the null hypothesis that the population correlation between X and Y is zero is also a test of the hypothesis that θ and φ are independent. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Scales and statistics: Parametric and nonparametric.

A comparison of parametric and nonparametric statistics is presented in terms of practical problems and measurement theoretical considerations. Though there is little to choose between the 2 in terms of significance level or power it is stated that parametric procedures, being more versatile, best meet the needs of psychological research. Type of metric scale, ordinal vs. interval, has little relevance to the question of whether to use parametric or nonparametric statistics. From Psyc Abstracts 36:01:3AE05A. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Meaningful inference: The role of measurement in statistics.

Stevens (1946) asserted that (a) consistency between the scale of measurement that generates a data set and the statistical/mathematical treatment that the data receive is necessary for interpretable results and (b) scale-analysis consistency is achievable by using techniques that are invariant with respect to admissible transformations. A number of individuals have argued for an alternative view that implies little or no relationship between scale of measurement and proper analytical techniques. These counterarguments are criticized. Stevens' original thesis is then reexamined in light of recent work on meaningfulness issues in measurement. The importance of measurement issues for an interpretable analysis is confirmed. However, the invariance criterion for selecting analytical techniques has severe problems, even though it is usually the only practicable criterion available. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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)     

分析数据的线性回归统计

简要介绍了怎样利用数理统计中的最小二乘法回归直线 ,代替日常检验中在坐标纸上绘制工作曲线 ,并用具体试验数据对回归直线进行显著性检验的方法、过程和计算进行阐述。     

Control of halo error: A multiple regression approach.

Comments on an article by F. J. Landy et al (see record 1981-00274-001) that suggests a method for excluding halo variance in rating scales. It is argued that this approach may result in excluding true variance. The present article presents a conceptualization of the halo effect in terms of a suppressor variable. Accordingly, a multiple regression approach for the treatment of halo variance is suggested. (13 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Testing multiple outcomes in repeated measures designs.

This study investigates procedures for controlling the familywise error rate (FWR) when testing hypotheses about multiple, correlated outcome variables in repeated measures (RM) designs. A content analysis of RM research articles published in 4 psychology journals revealed that 3 quarters of studies tested hypotheses about 2 or more outcome variables. Several procedures originally proposed for testing multiple outcomes in 2-group designs are extended to 2-group RM designs. The investigated procedures include 2 modified Bonferroni procedures that adjust the level of significance, α, for the effective number of outcomes and a permutation step-down (PSD) procedure. The FWR, any-variable power, and all-variable power are investigated in a Monte Carlo study. One modified Bonferroni procedure frequently resulted in inflated FWRs, whereas the PSD procedure controlled the FWR. The PSD procedure could be substantially more powerful than the conventional Bonferroni procedure, which does not account for dependencies among the outcome variables. However, the difference in power between the PSD procedure, which does account for these dependencies, and Hochberg's step-up procedure, which does not, were negligible. A numeric example illustrates implementation of these multiple-testing procedures. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Basic statistics and the inconsistency of multiple comparison procedures.

This paper has two main themes. First, the various statistical measures used in this journal are summarized and their interrelationships described by way of a flow chart. These are the pooled standard deviation, the pooled variance or mean square error (MSE), the standard error of each treatment mean (SEM) and of the difference between two treatment means (SED), and the least difference between two means which is significant at (e.g.) the 5% level of significance (LSD[5%]). The last three measures can be displayed as vertical bars in graphs, and the relationship between the lengths of these bars is graphically illustrated. It is suggested that the LSD is the most useful of these three measures. Second, when the experimenter has no prior hypotheses to be tested using analysis of variance "contrasts," a multiple comparison procedure (MCP) that examines all pair-wise differences between treatment means, may be appropriate. In this paper a fictitious experimental data set is used to compare several well-known MCPs by focussing on a particular operating known characteristic, the consistency of the results between an overall analysis of all treatments and an analysis of a subset of the experimental treatments... (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Monotonic hypotheses in multiple group designs: A Monte Carlo study.

Monotonic hypotheses are predictions about the ordering of group population means. A journal survey revealed that the problem was very common and that there was little uniformity among researchers regarding the statistical test to use. Most of the approaches in the literature to detect both monotonic trend and nonmonotonicity were compared under varying population conditions in a Monte Carlo simulation. The results suggested that only rarely will sample means order the same as the corresponding population means, leaving the approaches most researchers used with far too little power. Trend tests had far greater power; the one recommended is the familiar linear trend test. However, used alone this test does not detect the presence of any instances of nonmonotonicity. Therefore, it should be used in combination with a technique that can detect such inversions, preferably the Sidák-corrected reversal test conducted with a very high α (.50). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Faculty ratings and student grades: A university-wide multiple regression analysis.

Studied the relationship between grades professors give their students and ratings those students give their professors, using multivariate techniques and a large sample size (2,360 of 2,449 course sections taught in the spring semester of 1973) to avoid weaknesses of previous studies. Results show the following: (a) Only one factor was present among the 8 rating items; (b) the correlation between average student grade in each course section and average student rating of the teacher of that section was .35; (c) average grade was the best predictor of average rating; and (d) when average grade was added to several other available predictors, it significantly improved the multiple correlation from .25 to .39. Findings suggest that students' grades probably do influence their ratings of faculty, accounting for about 9% of the total variance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Disentangling the adult attention-deficit hyperactivity disorder endophenotype: Parametric measurement of attention.

Attention deficit hyperactivity disorder (ADHD) persists frequently into adulthood. The decomposition of endophenotypes by means of experimental neuro-cognitive assessment has the potential to improve diagnostic assessment, evaluation of treatment response, and disentanglement of genetic and environmental influences. We assessed four parameters of attentional capacity and selectivity derived from simple psychophysical tasks (verbal report of briefly presented letter displays) and based on a “theory of visual attention.” These parameters are mathematically independent, quantitative measures, and previous studies have shown that they are highly sensitive for subtle attention deficits. Potential reductions of attentional capacity, that is, of perceptual processing speed and working memory storage capacity, were assessed with a whole report paradigm. Furthermore, possible pathologies of attentional selectivity, that is, selection of task-relevant information and bias in the spatial distribution of attention, were measured with a partial report paradigm. A group of 30 unmedicated adult ADHD patients and a group of 30 demographically matched healthy controls were tested. ADHD patients showed significant reductions of working memory storage capacity of a moderate to large effect size. Perceptual processing speed, task-based, and spatial selection were unaffected. The results imply a working memory deficit as an important source of behavioral impairments. The theory of visual attention parameter working memory storage capacity might constitute a quantifiable and testable endophenotype of ADHD. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Ill-structured measurement designs in organizational research: Implications for estimating interrater reliability.

Organizational research and practice involving ratings are rife with what the authors term ill-structured measurement designs (ISMDs)--designs in which raters and ratees are neither fully crossed nor nested. This article explores the implications of ISMDs for estimating interrater reliability. The authors first provide a mock example that illustrates potential problems that ISMDs create for common reliability estimators (e.g., Pearson correlations, intraclass correlations). Next, the authors propose an alternative reliability estimator--G(q,k)--that resolves problems with traditional estimators and is equally appropriate for crossed, nested, and ill-structured designs. By using Monte Carlo simulation, the authors evaluate the accuracy of traditional reliability estimators compared with that of G(q,k) for ratings arising from ISMDs. Regardless of condition, G(q,k) yielded estimates as precise or more precise than those of traditional estimators. The advantage of G(q,k) over the traditional estimators became more pronounced with increases in the (a) overlap between the sets of raters that rated each ratee and (b) ratio of rater main effect variance to true score variance. Discussion focuses on implications of this work for organizational research and practice. (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)     

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)     

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)     

基于正交试验与回归统计方法的露天矿运输扬尘影响因素分析

露天矿运输过程中,车速、路面粉尘负荷、粉尘含水率与扬尘浓度有密切的关系.通过对它们设计正交试验和多元回归统计分析,得到运输扬尘质量浓度的计量模型,该模型具有99%的显著性和拟合性.经回归系数显著性检验,自变量对因变量的影响均极为显著,其显著性大小依次为:车速、粉尘含水率和路面粉尘负荷.最后采用实测值与模型计算值的计算机拟合,对模型进行验证.结果表明,该模型具有较高的可信度.     

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)     

Dissociating conflict adaptation from feature integration: A multiple regression approach.

Congruency effects are typically smaller after incongruent than after congruent trials. One explanation is in terms of higher levels of cognitive control after detection of conflict (conflict adaptation; e.g., M. M. Botvinick, T. S. Braver, D. M. Barch, C. S. Carter, & J. D. Cohen, 2001). An alternative explanation for these results is based on feature repetition and/or integration effects (e.g., B. Hommel, R. W. Proctor, & K.-P. Vu, 2004; U. Mayr, E. Awh, & P. Laurey, 2003). Previous attempts to dissociate feature integration from conflict adaptation focused on a particular subset of the data in which feature transitions were held constant (J. G. Kerns et al., 2004) or in which congruency transitions were held constant (C. Akcay & E. Hazeltine, in press), but this has a number of disadvantages. In this article, the authors present a multiple regression solution for this problem and discuss its possibilities and pitfalls. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Failure to complete treatment for headache: A multiple regression analysis.

Used a hierarchical multiple regression analysis to predict the number of sessions completed by 90 patients in a headache-treatment program. Demographic variables accounted for 18% of the variance (p?p?