Publication bias in the economic freedom and economic growth literature

The impact of institutions on economic performance has attracted significant attention from researchers, as well as from policy reformers. A rapidly growing area in this literature is the impact of economic freedom on economic growth. The aim of this paper was to explore publication bias in this literature by means of traditional funnel plots, meta‐significance testing, as well as by bootstrapping these meta‐significance tests. When all the available estimates are combined and averaged, there seems to be evidence of a genuine and positive economic freedom – economic growth effect. However, it is also shown that the economic freedom – economic growth literature is tainted strongly with publication bias. The existence of publication bias makes it difficult to identify the magnitude of the genuine effect of economic freedom on economic growth. The paper explores the differences between aggregate and disaggregate measures of economic freedom and shows that selection effects are stronger when aggregate measures are used.

Publication bias in union-productivity research?

This paper develops and applies several meta-analytic techniques to investigate the presence of publication bias in industrial relations research, specifically in the union-productivity effects literature. Publication bias arises when statistically insignificant results are suppressed or when results satisfying prior expectations are given preference. Like most fields, research in industrial relations is vulnerable to publication bias. Unlike other fields such as economics, there is no evidence of publication bias in the union-productivity literature, as a whole. However, there are pockets of publication selection, as well as negative autoregression, confirming the controversial nature of this area of research. Meta-regression analysis reveals evidence of publication bias (or selection) among US studies.

Factors associated with functioning style and coping strategies of familes with a child with an autism spectrum disorder

A survey of parents/caregivers of a child with an autism spectrum disorder (ASD) was conducted to examine the relationship between ASD characteristics, family functioning and coping strategies. Having a child with ASD places considerable stress on the family. Primary caregivers of a child with ASD from a regional and rural area in Victoria, Australia (N = 53) were surveyed concerning their child with ASD, family functioning (adaptability and cohesion), marital satisfaction, self-esteem and coping strategies. Results suggest that these caregivers had healthy self-esteem, although they reported somewhat lower marital happiness, family cohesion and family adaptability than did norm groups. Coping strategies were not significant predictors of these outcome variables. Results highlight the need for support programmes to target family and relationship variables as well as ASD children and their behaviours, in order to sustain the family unit and improve quality of life for parents and caregivers as well as those children.

Stalking, and social and romantic functioning among adolescents and adults with autism spectrum disorder

We examine the nature and predictors of social and romantic functioning in adolescents and adults with ASD. Parental reports were obtained for 25 ASD adolescents and adults (13–36 years), and 38 typical adolescents and adults (13–30 years). The ASD group relied less upon peers and friends for social (OR = 52.16, p < .01) and romantic learning (OR = 38.25, p < .01). Individuals with ASD were more likely to engage in inappropriate courting behaviours (χ² df = 19 = 3168.74, p < .001) and were more likely to focus their attention upon celebrities, strangers, colleagues, and ex-partners (χ2 df = 5 = 2335.40, p < .001), and to pursue their target longer than controls (t = −2.23, df = 18.79, p < .05). These results show that the diagnosis of ASD is pertinent when individuals are prosecuted under stalking legislation in various jurisdictions.

The need for low bias algorithms in classification learning from large data sets

This paper reviews the appropriateness for application to large data sets of standard machine learning algorithms, which were mainly developed in the context of small data sets. Sampling and parallelisation have proved useful means for reducing computation time when learning from large data sets. However, such methods assume that algorithms that were designed for use with what are now considered small data sets are also fundamentally suitable for large data sets. It is plausible that optimal learning from large data sets requires a different type of algorithm to optimal learning from small data sets. This paper investigates one respect in which data set size may affect the requirements of a learning algorithm — the bias plus variance decomposition of classification error. Experiments show that learning from large data sets may be more effective when using an algorithm that places greater emphasis on bias management, rather than variance management.

Publication bias in industrial relations research

Publication bias arises when statistically non-significant results are suppressed or when only results satisfying prior expectations are published. Like most fields, research in industrial relations is vulnerable to publication bias. In this paper qualitative and quantitative techniques are used in order to detect publication bias in the union-productivity effects literature. We find no evidence of publication bias in this literature, although there does appear to be autoregression in the published results.