About: Variance (accounting) is a research topic. Over the lifetime, 9521 publications have been published within this topic receiving 314809 citations.
Papers published on a yearly basis
01 Jan 1969
TL;DR: In this paper, the authors present a model for the analysis of variance in a single-classification and two-way and multiway analysis of Variance with the assumption of correlation.
Abstract: 1. Introduction 2. Data in Biology 3. Computers and Data Analysis 4. Descriptive Statistics 5. Introduction to Probability Distributions 6. The Normal Probability Distribution 7. Hypothesis Testing and Interval Estimation 8. Introduction to Analysis of Variance 9. Single-Classification Analysis of Variance 10. Nested Analysis of Variance 11. Two-Way and Multiway Analysis of Variance 12. Statistical Power and Sample Size in the Analysis of Variance 13. Assumptions of Analysis of Variance 14. Linear Regression 15. Correlation 16. Multiple and Curvilinear Regression 17. Analysis of Frequencies 18. Meta-Analysis and Miscellaneous Methods
TL;DR: A quantitative integration and review of research on the Theory of Planned Behaviour and the subjective norm, which found that intentions and self-predictions were better predictors of behaviour than attitude, subjective norm and PBC.
Abstract: The Theory of Planned Behaviour (TPB) has received considerable attention in the literature. The present study is a quantitative integration and review of that research. From a database of 185 independent studies published up to the end of 1997, the TPB accounted for 27% and 39% of the variance in behaviour and intention, respectively. The perceived behavioural control (PBC) construct accounted for significant amounts of variance in intention and behaviour, independent of theory of reasoned action variables. When behaviour measures were self-reports, the TPB accounted for 11% more of the variance in behaviour than when behaviour measures were objective or observed (R2s = .31 and .21, respectively). Attitude, subjective norm and PBC account for significantly more of the variance in individuals' desires than intentions or self-predictions, but intentions and self-predictions were better predictors of behaviour. The subjective norm construct is generally found to be a weak predictor of intentions. This is partly attributable to a combination of poor measurement and the need for expansion of the normative component. The discussion focuses on ways in which current TPB research can be taken forward in the light of the present review.
TL;DR: In this article, the authors examined the effect of the variance inflation factor (VIF) on the results of regression analyses, and found that threshold values of the VIF need to be evaluated in the context of several other factors that influence the variance of regression coefficients.
Abstract: The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index.
TL;DR: In this paper, the basic theory of analysis of variance by considering several different mathematical models is examined, including fixed-effects models with independent observations of equal variance and other models with different observations of variance.
Abstract: Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.
TL;DR: The authors argued that the popular position that common method variance automatically affects variables measured with the same method is a distortion and oversimplification of the true state of affairs, reaching the status of urban legend.
Abstract: It has become widely accepted that correlations between variables measured with the same method, usually self-report surveys, are inflated due to the action of common method variance (CMV), despite a number of sources that suggest the problem is overstated. The author argues that the popular position suggesting CMV automatically affects variables measured with the same method is a distortion and oversimplification of the true state of affairs, reaching the status of urban legend. Empirical evidence is discussed casting doubt that the method itself produces systematic variance in observations that inflates correlations to any significant degree. It is suggested that the term common method variance be abandoned in favor of a focus on measurement bias that is the product of the interplay of constructs and methods by which they are assessed. A complex approach to dealing with potential biases involves their identification and control to rule them out as explanations for observed relationships using a variety ...
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