Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers. This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model. With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.
This program is specifically intended for adolescents suffering from posttraumatic stress disorder. Clients are exposed to safe but anxiety-provoking situations as a way of overcoming their trauma-related fears. Recounting the memory of the trauma also helps clients emotionally process their traumatic experiences in order to diminish PTSD symptoms. The workbook is designed for adolescent use and includes teen-friendly forms to reinforce the skills learned in therapy.
This treatment program adapts the principles of Dr. Foa's proven effective Prolonged Exposure Therapy for adolescents suffering from Posttraumatic Stress Disorder (PTSD.) The treatment program is based on the principles of prolonged exposure and emotional processing for use with those individuals who suffer from PTSD. In vivo and imaginal exposure comprise the core of the treatment, along with breathing retraining and techniques for monitoring progress. The treatment is presented in modules that can be individually tailored to fit the needs of each patient. Because many adolescent PTSD sufferers do not initiate therapy on their own, but are referred to therapy by social workers, parents, or other authority figures, their willingness to participate in their treatment can vary widely. The first element of this treatment, serves to assess the client's attitude, and increase motivation to change. Other modules introduce psychoeducation, real-life exposure, emotional processing, and relapse prevention. This companion workbook provides additional information, monitoring forms, and worksheets to help clients take control of their treatment.
Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers. This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model. With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.