Emotion understanding and maternal sensitivity as protective factors against hostile attribution bias in anger‐prone children

The present research examined children’s anger proneness, emotion understanding, and maternal sensitivity during toddlerhood as predictors of children’s hostile attribution bias (HAB) during the later preschool years. At 2.8 years (N = 128), maternal sensitivity (e.g., child‐centered behavior) was observed during mother–child play and snack, and parents reported on children’s anger proneness. At 3.3 years, emotion understanding (i.e., ability to identify emotional expressions accurately) was measured via an interactive puppet interview. At 4.8 and 5.4 years, children's HAB was assessed via child responses to hypothetical vignettes of ambiguous peer provocations. Path models revealed that maternal sensitivity predicted fewer hostile attributions. In addition, emotion understanding and maternal sensitivity emerged as buffers against the negative effect of anger proneness on HAB. Specifically, greater anger proneness was associated with more frequent hostile attributions, but only when children had lower emotion understanding or had mothers who were less sensitive. The findings highlight the interplay between intrapersonal and interpersonal factors in early childhood that contribute to a hostile attribution bias during the preschool period.     

Aberration of the Citation

Multiple inherent biases related to different citation practices (for e.g., self-citations, negative citations, wrong citations, multi-authorship-biased citations, honorary citations, circumstantial citations, discriminatory citations, selective and arbitrary citations, etc.) make citation-based bibliometrics strongly flawed and defective measures. A paper can be highly cited for a while (for e.g., under circumstantial or transitional knowledge), but years later it may appear that its findings, paradigms, or theories were untrue or invalid anymore. By contrast, a paper may remain shelved or overlooked for years or decades, but new studies or discoveries may actualize its subject at any moment. As citation-based metrics are transformed into “commercial activities,” the “citation credit” should be considered on a commercial basis too, in the sense that “citation credit” should be shared out as a “citation dividend” by shareholders (coauthors) averagely or proportionally to their contributions but not fully appropriated by each of them. At equal numbers of citations, the greater number of authors, the lower “citation credit” should be and vice versa. Overlooking the presence of distorted and subjective citation practices makes many people and administrators “obsessed” with the number of citations to such an extent to run after “highly cited” authors and to create specialized citation databases for commercial purposes. Citation-based bibliometrics, however, are unreliable and unscientific measures; citation counts do not mean that a more cited work is of a higher quality or accuracy than a less cited work because citations do not measure the quality or accuracy. Citations do not mean that a highly cited author or journal is more commendable than a less cited author or journal. Citations are not more than countable numbers: no more, no less.     

On Comparing Apples with Apples Deemed Nonexistent: A Reply to Johnson

ABSTRACT

M. Johnson's rebuttal is an example of the paradigm I described to readers in my original article. Johnson develops a taxonomy of intimate violence where female initiated violence does not exist. He does this despite extensive evidence to the contrary. A recent national survey included questions about both severity and instrumentality of violence given to both genders. Intimate terrorism (as defined by a combination of instrumentality and repletion of severe violence) is perpetrated by both genders (2.6% of women and 4.2% of men). The notion that women do not initiate domestic violence is misleading to custody assessors who must consider risk to children as part of the child's best interest.     

Generalizing from PRSA to public relations: How to accommodate sampling bias in public relations scholarship

One of the major challenges to conducting externally valid, quantitative scholarship in public relations is accessing samples of practitioners that are willing to participate in academic research. One sampling frame would naturally be the membership of the Public Relations Society of America (PRSA), which is the world's largest professional association for public relations practitioners. Yet, even if the question of access were resolved, there still remains the question of external validity, i.e., the issue of whether and to what extent the membership of PRSA (or any other sampling frame) reflects the population of public relations practitioners in general.     

Increasing minority employment: Are you ready to recruit?

To increase employment from desired race or gender groups, employers nearly always first turn to recruiting from outside their organization. But a few years after such initiatives are undertaken, diversity numbers typically remain low or even decrease, turnover among recruits from the sought‐after groups is high, and the efforts are threatened by their recurrent cost. Employers need to break this fruitless cycle by thinking more strategically. Without an inclusive organizational climate that retains and fully utilizes minority employees after hire, simply recruiting more such employees will not lead to sustainable changes in workforce demographics. Drawing on empirical research, this paper describes six “red flags” that identify workplaces not ready to recruit. Only after organizational changes address the deficiencies identified by the red flags will the time for minority recruitment be at hand. But by then special focused recruitment may not be necessary; when employers change their workplace cultures to become truly inclusive, word gets around.     

The Validity of Social Media–Based Career Information

The use of social media expands the availability and sources of career information. However, the authorship of this information has changed from traditional print media and multimedia sources created by experts to social media–based career information created by the users themselves. Although variability in career information validity has been an issue for some time, rapid growth in the use of social media creates some unique challenges. The ease with which social media–based career information can spread creates the potential for rapid widespread dissemination of disinformation and biased perceptions. Potential sources of invalidity include intentional bias (with or without profit motive), unintentional bias, restricted range of experience, out‐of‐date information, popularity bias, similarity bias, and context deficiency. The authors examine potential sources of social media–based career information invalidity and suggest implications for practice to help individuals make the best use of such information.     

SIZE CHARACTERISTICS OF TESTS FOR SAMPLE SELECTION BIAS: A MONTE CARLO COMPARISON AND EMPIRICAL EXAMPLE

The t-test of an individual coefficient is used widely in models of qualitative choice. However, it is well known that the t-test can yield misleading results when the sample size is small. This paper provides some experimental evidence on the finite sample properties of the t-test in models with sample selection biases, through a comparison of the t-test with the likelihood ratio and Lagrange multiplier tests, which are asymptotically equivalent to the squared t-test. The finite sample problems with the t-test are shown to be alarming, and much more serious than in models such as binary choice models. An empirical example is also presented to highlight the differences in the calculated test statistics.     

Bias reduction of maximum likelihood estimates for a modified skew-normal distribution

ABSTRACT

This paper presents a modified skew-normal (SN) model that contains the normal model as a special case. Unlike the usual SN model, the Fisher information matrix of the proposed model is always non-singular. Despite of this desirable property for the regular asymptotic inference, as with the SN model, in the considered model the divergence of the maximum likelihood estimator (MLE) of the skewness parameter may occur with positive probability in samples with moderate sizes. As a solution to this problem, a modified score function is used for the estimation of the skewness parameter. It is proved that the modified MLE is always finite. The quasi-likelihood approach is considered to build confidence intervals. When the model includes location and scale parameters, the proposed method is combined with the unmodified maximum likelihood estimates of these parameters.     

Bias reduction of the maximum-likelihood estimator for a conditional Gaussian MA(1) model

In this paper, we consider an estimation for the unknown parameters of a conditional Gaussian MA(1) model. In the majority of cases, a maximum-likelihood estimator is chosen because the estimator is consistent. However, for small sample sizes the error is large, because the estimator has a bias of O(n^{? 1}). Therefore, we provide a bias of O(n^{? 1}) for the maximum-likelihood estimator for the conditional Gaussian MA(1) model. Moreover, we propose new estimators for the unknown parameters of the conditional Gaussian MA(1) model based on the bias of O(n^{? 1}). We investigate the properties of the bias, as well as the asymptotical variance of the maximum-likelihood estimators for the unknown parameters, by performing some simulations. Finally, we demonstrate the validity of the new estimators through this simulation study.