There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd.

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A basic introduction to fixed‐effect and random‐effects models for meta‐analysis

Problem: Localities and states are turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies. Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection. Methods: We computed elasticities for individual studies and pooled them to produce weighted averages. Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magnitude greater than 0.39, and most are much less. Still, the combined effect o...

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Travel and the Built Environment

The seven articles in this issue, and the accompanying meta-analysis in Health Psychology Review [McEachan, R.R.C., Conner, M., Taylor, N., & Lawton, R.J. (2011). Prospective prediction of health-related behaviors with the theory of planned behavior: A meta-analysis. Health Psychology Review, 5, 97–144], illustrate the wide application of the theory of planned behaviour [Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211] in the health domain. In this editorial, Ajzen reflects on some of the issues raised by the different authors. Among the topics addressed are the nature of intentions and the limits of predictive validity; rationality, affect and emotions; past behaviour and habit; the prototype/willingness model; and the role of such background factors as the big five personality traits and social comparison tendency.

https://www.tandfonline.com/doi/pdf/10.1080/08870446.2011.613995

The theory of planned behaviour: reactions and reflections

This article describes the control-value theory of achievement emotions and its implications for educational research and practice. The theory provides an integrative framework for analyzing the antecedents and effects of emotions experienced in achievement and academic settings. It is based on the premise that appraisals of control and values are central to the arousal of achievement emotions, including activity-related emotions such as enjoyment, frustration, and boredom experienced at learning, as well as outcome emotions such as joy, hope, pride, anxiety, hopelessness, shame, and anger relating to success or failure. Corollaries of the theory pertain to the multiplicity and domain specificity of achievement emotions; to their more distal individual and social antecedents, their effects on engagement and achievement, and the reciprocal linkages between emotions, antecedents and effects; to the regulation and development of these emotions; and to their relative universality across genders and cultures. Implications addressed concern the conceptual integration of emotion, motivation, and cognition, and the need to advance mixed-method paradigms. In closing, implications for educational practice are discussed.

The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice

(1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.

Human cognitive abilities: A survey of factor analytic studies

Onthebasisofameta-analysisofpairwisecorrelationsbetweenwork- ing memory tasks and cognitive ability measures, P. L. Ackerman, M. E. Beier, and M. O. Boyle (2005) claimed that working memory capacity (WMC) shares less than 25% of its variance with general intelligence (g) and with reasoning ability. In this comment, the authors argue that this is an underestimation because of several methodological shortcomings and biases. A reanalysis of the data reported in Ack- erman et al. using the correct statistical procedures demonstrates that g and WMC are very highly correlated. On a conceptual level, the authors point out that WMC should be regarded as an explanatory construct for intellectual abilities. Theories of working memory do not claim that WMC is isomorphic with intelligence factors but that it is a very strong predictor of reasoning ability and also predicts general fluid intelligence and g.

http://jtoomim.org/brain-training/working%20memory%20and%20intelligence%20their%20correlation%20and%20their%20relation%20comment%20on%20ackerman,beier%20and%20boyle.pdf

Working Memory and Intelligence—Their Correlation and Their Relation: Comment on Ackerman, Beier, and Boyle (2005)

Emotional intelligence (EI) may predict stress responses and coping strategies in a variety of applied settings. This study compares EI and the personality factors of the Five Factor Model (FFM) as predictors of task-induced stress responses. Participants (N = 200) were randomly assigned to 1 of 4 task conditions, 3 of which were designed to be stressful. Results confirmed that low EI was related to worry states and avoidance coping, even with the FFM statistically controlled. However, EI was not specifically related to task-induced changes in stress state. Results also confirmed that Neuroticism related to distress, worry, and emotion-focused coping, and Conscientiousness predicted use of task-focused coping. The applied utility of EI and personality measures is discussed.

Emotional intelligence, personality, and task-induced stress

Preface Introduction Theory: Statistical Methods of Meta-Analysis Effect Sizes Families of Effect Sizes The r Family: Correlation Coefficients as Effect Sizes The d Family: Standardized Mean Differences as Effect Sizes Conversion of Effect Sizes A General Framework of Meta-Analysis Fixed Effects Model Random Effects Model Mixture Models Classes of Situations for the Application of Meta-Analysis Approaches to Meta-Analysis Hedges and Olkin Procedures for r as Effect Size Procedures for d as Effect Size Rosenthal and Rubin Hunter and Schmidt Refined Approaches DerSimonian-Laird Olkin and Pratt Changes in Parameters to be Estimated by the Choice of an Approach Comparisons of the Approaches Summary Method: Monte Carlo Study Aims and General Procedure Distributions in the Universe of Studies Parameters Drawing Random Correlation Coefficients Approximations to the Sampling Distribution of r Evaluation of the Approximations Details of Programming Summary Results Preliminaries Estimation of Parameter [mu]r Bias and Accuracy Homogeneous Situation S1 Heterogeneous Situation S2 Heterogeneous Situation S3 Relative Efficiency of the Estimators Significance Tests: Testing [mu]r = 0 Confidence Intervals Homogeneity Tests The Q-Test Homogeneous Situation S1: Type I Error Rates Heterogeneous Situations S2 and S3: Power The Hunter-Schmidt Approach to the Test of Homogeneity: The 75 per cent and 90 per cent Rule Estimates of Heterogeneity Variance Homogeneous situation S1 Heterogeneous Situations S2 and S3 Summary Discussion List of Figures List of Tables Nomenclature References Appendix A: Technical Details of the Simulation Procedure Beta Distributions in the Universe of Effect Sizes An Annotated Mathematica[trademark] Notebook for a Comparison of Approximations to the Exact Density of R Appendix B: Tables and Figures of Results Estimation of the Parameter [mu]r Subject Index Author Index

Meta-Analysis: A Comparison of Approaches

Emotions measures represent an important means of obtaining construct validity evidence for emotional intelligence (EI) tests because they have the same theoretical underpinnings. Additionally, the extent to which both emotions and EI measures relate to intelligence is poorly understood. The current study was designed to address these issues. Participants (N = 138) completed the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), two emotions measures, as well as four intelligence tests. Results provide mixed support for the model hypothesized to underlie the MSCEIT, with emotions research and EI measures failing to load on the same factor. The emotions measures loaded on the same factor as intelligence measures. The validity of certain EI components (in particular, Emotion Perception), as currently assessed, appears equivocal.

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Exploring the validity of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) with established emotions measures.

Foreword. Peter Salovey. Preface. Contributors. Part I Introduction. 1 Theory, Measurement, and Applications of Emotional Intelligence: Frames of Reference. Ralf Schulze, Richard D. Roberts, Gerald Matthews, and Moshe Zeidner. Part II Theories of Emotional Intelligence. 2 Models of Emotional Intelligence. Aljoscha C. Neubauer and H. Harald Freudenthaler. 3 The Emotion Systems and the Development of Emotional Intelligence. David Schultz, Carroll E. Izard, and Jo Ann A. Abe. 4 Mindfulness-Based Emotional Intelligence: A Theory and Review of the Literature. Joseph Ciarrochi and Claire Godsell. 5 Social and Emotional Intelligences: Starting a Conversation about Their Similarities and Differences. Sun-Mee Kang, Jeanne D. Day, and Naomi M. Meara. 6 Far Too Many Intelligences? On the Communalities and Differences Between Social, Practical, and Emotional Intelligences. Elizabeth J. Austin and Donald H. Saklofske. Part III Measures of Emotional Intelligence. 7 Measures of Emotional Intelligence: Practice and Standards. Oliver Wilhelm. 8 Using Consensus Based Measurement to Assess Emotional Intelligence. Peter J. Legree, Joseph Psotka, Trueman Tremble, and Dennis R. Bourne. 9 Measuring Trait Emotional Intelligence. Juan Carlos Perez, K. V. Petrides, and Adrian Furnham. 10 Social Intelligence-A Review and Critical Discussion of Measurement Concepts. Susanne Weis and Heinz-Martin S??. Part IV Applications of Emotional Intelligence. 11 Emotional Intelligence in the Context of Learning and Achievement. Thomas Goetz, Christiane Zirngibl, Reinhard Pekrun, and Nathan Hall. 12 Emotional Intelligence in the Workplace: A Review and Synthesis. Rebecca Abraham. 13 The Relevance of Emotional Intelligence for Clinical Psychology. James D. A. Parker. 14 Emotional Intelligence and Inter-Personal Skills. Elisabeth Engelberg and Lennart Sj?berg. Part V Conclusion. 15 Understanding, Measuring, and Applying Emotional Intelligence: What Have We Learned? What Have We Missed?. Richard D. Roberts, Ralf Schulze, Gerald Matthews, and Moshe Zeidner. Subject Index. Author Index

Ralf Schulze

Papers

Working Memory and Intelligence—Their Correlation and Their Relation: Comment on Ackerman, Beier, and Boyle (2005)

Emotional intelligence, personality, and task-induced stress

Meta-Analysis: A Comparison of Approaches

Exploring the validity of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) with established emotions measures.

Emotional Intelligence: An International Handbook