The value of RCT evidence depends on the quality of statistical analysis

The authors examined statistical practices in 193 randomized controlled trials (RCTs) of psychological therapies published in prominent psychology and psychiatry journals during 1999-2003. Statistical significance tests were used in 99% of RCTs, 84% discussed clinical significance, but only 46% considered-even minimally-statistical power, 31% interpreted effect size and only 2% interpreted confidence intervals. In a second study, 42 respondents to an email survey of the authors of RCTs analyzed in the first study indicated they consider it very important to know the magnitude and clinical importance of the effect, in addition to whether a treatment effect exists. The present authors conclude that published RCTs focus on statistical significance tests ("Is there an effect or difference?"), and neglect other important questions: "How large is the effect?" and "Is the effect clinically important?" They advocate improved statistical reporting of RCTs especially by reporting and interpreting clinical significance, effect sizes and confidence intervals.     

Collective Apologies and Their Effects on Forgiveness: Pessimistic Evidence but Constructive Implications

In the last three decades, there has been an explosion in the frequency with which leaders of groups have issued official apologies for collective transgressions. These apologies are commonly assumed to lay a pathway to forgiveness and reconciliation, but empirical examination of the downstream consequences of collective apologies is still in its infancy. In this article, we review a series of studies—including interview studies, survey studies, and experiments—that question the assumed wisdom that collective apologies lead to intergroup forgiveness. Reasons for the muted evidence of an apology–forgiveness link at the intergroup level are elaborated, and implications for how best to issue gestures of reconciliation and remorse are discussed.     

Expansion of Genetic Services Utilizing a General Genetic Counseling Clinic

We established a general genetic counseling clinic (GCC) to help reduce long wait times for new patient appointments and to enhance services for a subset of patients. Genetic counselors, who are licensed in Tennessee, were the primary providers and MD geneticists served as medical advisors. This article describes the clinic referral sources, reasons for referral and patient dispositions following their GCC visit(s). We obtained patients by triaging referrals made to our medical genetics division. Over 24 months, our GCC provided timely visits for 321 patients, allowing the MD geneticists to focus on patients needing a clinical exam and/or complex medical management. Following their GCC visit(s), over 80 % of patients did not need additional appointments with an MD geneticist. The GCC allowed the genetic counselor to spend more time with patients than is possible in our traditional medical genetics clinic. Patient satisfaction surveys (n?=?30) were very positive overall concerning the care provided. Added benefits for the genetic counselors were increased professional responsibility, autonomy and visibility as health care providers. We conclude that genetic counselors are accepted as health care providers by patients and referring providers for a subset of clinical genetics cases. A GCC can expand genetic services, complement more traditional genetic clinic models and utilize the strengths of the genetic counselor health care provider.     

The statistical recommendations of the American Psychological Association Publication Manual: Effect sizes,confidence intervals,and meta‐analysis

Estimation based on effect sizes, confidence intervals, and meta‐analysis usually provides a more informative analysis of empirical results than does statistical significance testing, which has long been the conventional choice in psychology. The sixth edition of the American Psychological Association Publication Manual now recommends that psychologists should, wherever possible, use estimation and base their interpretation of research results on point and interval estimates. We outline the Manual's recommendations and suggest how they can be put into practice: adopt an estimation framework, starting with the formulation of research aims as ‘How much?’ or ‘To what extent?’ questions. Calculate from your data effect size estimates and confidence intervals to answer those questions, then interpret. Wherever appropriate, use meta‐analysis to integrate evidence over studies. The Manual's recommendations can assist psychologists improve they way they do their statistics and help build a more quantitative and cumulative discipline.