Empirical research on fantasy proneness and its correlates 2000–2018: A meta-analysis.

2021 ◽  
Author(s):  
Harald Merckelbach ◽  
Henry Otgaar ◽  
Steven Jay Lynn
Keyword(s):  
Empirical Research ◽  
Meta Analysis ◽  
Fantasy Proneness

Which Data to Meta-Analyze, and How?

2019 ◽  
Vol 227 (1) ◽  
pp. 64-82 ◽  
Author(s):  
Martin Voracek ◽  
Michael Kossmeier ◽  
Ulrich S. Tran
Keyword(s):  
Empirical Research ◽  
Meta Analysis ◽  
Curve Analysis ◽  
Research Progress ◽  
Primary Data ◽  
Statistical Procedures ◽  
Graphical Displays ◽  
Primary Data Analysis ◽  
Meta Analyses ◽  
Data Analytic

Abstract. Which data to analyze, and how, are fundamental questions of all empirical research. As there are always numerous flexibilities in data-analytic decisions (a “garden of forking paths”), this poses perennial problems to all empirical research. Specification-curve analysis and multiverse analysis have recently been proposed as solutions to these issues. Building on the structural analogies between primary data analysis and meta-analysis, we transform and adapt these approaches to the meta-analytic level, in tandem with combinatorial meta-analysis. We explain the rationale of this idea, suggest descriptive and inferential statistical procedures, as well as graphical displays, provide code for meta-analytic practitioners to generate and use these, and present a fully worked real example from digit ratio (2D:4D) research, totaling 1,592 meta-analytic specifications. Specification-curve and multiverse meta-analysis holds promise to resolve conflicting meta-analyses, contested evidence, controversial empirical literatures, and polarized research, and to mitigate the associated detrimental effects of these phenomena on research progress.

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

M. Meta-Analysis

2021 ◽  
pp. 163-165
Author(s):  
Noémie Laurens
Keyword(s):  
Literature Review ◽  
Empirical Research ◽  
Effect Size ◽  
Meta Analysis ◽  
Research Synthesis ◽  
Effect Sizes ◽  
Primary Research ◽  
Unit Of Analysis ◽  
Research Studies

This chapter illustrates meta-analysis, which is a specific type of literature review, and more precisely a type of research synthesis, alongside traditional narrative reviews. Unlike in primary research, the unit of analysis of a meta-analysis is the results of individual studies. And unlike traditional reviews, meta-analysis only applies to: empirical research studies with quantitative findings hat are conceptually comparable and configured in similar statistical forms. What further distinguishes meta-analysis from other research syntheses is the method of synthesizing the results of studies — i.e. the use of statistics and, in particular, of effect sizes. An effect size represents the degree to which the phenomenon under study exists.

Sexual Orientation

2019 ◽  
pp. 264-295
Author(s):  
Bonnie Moradi ◽  
Stephanie L. Budge
Keyword(s):  
Sexual Orientation ◽  
Empirical Research ◽  
Meta Analysis ◽  
Sexual Orientations ◽  
Orientation Group

The clinical need for lesbian, gay, bisexual, and queer (LGBQ+) affirmative psychotherapies has been widely recognized; however, empirical research on the outcomes of such psychotherapies is limited. This chapter begins by offering definitions and delineating four key themes of LGBQ+ affirmative psychotherapies. The authors conceptualize LGBQ+ affirmative psychotherapies not as sexual orientation group specific but rather as considerations and practices that can be applied with all clients. The chapter then summarizes a search for studies to attempt a meta-analysis and discusses limitations and directions for research based on this search. The chapter ends by delineating diversity considerations and recommending therapeutic practices for advancing affirmative psychotherapy with clients of all sexual orientations.

The Real Relationship

2019 ◽  
pp. 351-378
Author(s):  
Charles J. Gelso ◽  
Dennis M. Kivlighan ◽  
Rayna D. Markin
Keyword(s):  
Confidence Interval ◽  
Empirical Research ◽  
Meta Analysis ◽  
Outcome Studies ◽  
Personal Relationship ◽  
The Other ◽  
Real Relationship ◽  
The Real ◽  
Relationship Outcome ◽  
Talking Cure

Although writing about the real relationship has existed from the beginnings of the talking cure,” it is only in recent years that empirical research has focused on this phenomenon. The real relationship is the personal relationship between patient and therapist marked by the extent to which each is genuine with the other and perceives/experiences the other in ways that are realistic. The strength of the real relationship is determined by both the extent to which it exists and the degree to which it is positive or favorable. In this chapter, a meta-analysis is presented on the association between the real relationship and the outcome of psychotherapy. Summed across 16 studies, this meta-analysis revealed a moderate association with outcome (r =.38, 95% confidence interval [.30, .47], p<.001, d = 0.80, N = 1,502 participants). This real relationship–outcome association was independent of the type of outcome studies and of the source of the measure. We also present frequent measures of the real relationship, limitations of the research, and patient contributions. The chapter concludes with diversity considerations and practice recommendations for developing and strengthening the real relationship.

Weight- and meta-analysis of empirical literature on entrepreneurship

2017 ◽  
Vol 18 (3) ◽  
pp. 195-209 ◽  
Author(s):  
Adel Alferaih
Keyword(s):  
Empirical Research ◽  
Meta Analysis ◽  
Empirical Studies ◽  
Entrepreneurial Intentions ◽  
Empirical Literature ◽  
Research Model ◽  
Entrepreneurship Research ◽  
Meta Analyses

While a number of studies have reviewed empirical research on individuals’ entrepreneurial intentions (EIs), very little is known about the cumulative performance of frequently used constructs and their direct and indirect relationships with EI. This research has exposed 123 usable empirical studies of EI to weight- and meta-analyses to determine the cumulative performance of various frequently explored relationships. A generic research model of the antecedents of EI is proposed on that basis. The outcomes of this research and its limitations have practical and theoretical implications for future entrepreneurship research.

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