Robust Statistics

In lieu of an abstract, here is the entry\u27s first paragraph: Robust statistics are procedures that maintain nominal Type I error rates and statistical power in the presence of violations of the assumptions that underpin parametric inferential statistics. Since George Box coined the term in 1953, research on robust statistics has centered on the assumption of normality, although the violation of other parametric assumptions (e.g., homogeneity of variance) has their own implications for the accuracy of parametric procedures. This entry looks at the importance of robust statistics in educational and social science research and explains the robustness argument. It then describes robust descriptive statistics, their inferential extensions, and two common resampling procedures that are robust alternatives to classic parametric methods

Winsorizing

In lieu of an abstract, here is the entry\u27s first paragraph: Winsorizing is a procedure that moderates the influence of outliers on the mean and variance and thereby creates more robust estimators of location and variability. The procedure is named for biostatistician Charles P. Winsor. Parametric inferential procedures that rely on the mean and variance (e.g., t test) become more robust when they incorporate Winsorized estimators. Winsorizing is an important tool for educational and social science researchers for two reasons. First, significance tests based on the mean and variance are very common procedures for significance testing in the social sciences. Second, surveys of the educational and psychological literature show that nonnormally distributed data are the rule rather than the exception, and even modest departures from normality disproportionately affect the mean and variance compared with other more robust estimators of location (e.g., median) and variability (e.g., median absolute deviation

Reaching out to behavioral science audiences via meta-analysis

Exposing students to meta-analysis supports ASA Curriculum Guidelines regarding the importance of data science, working with real and unusual data, diverse approaches to statistical models, and building relationships with allied disciplines. This poster present some of the benefits to students from taking an undergraduate meta-analysis course. The course is a 300-level statistics elective (class size: 12-18) that enrolls statistics majors and students from social and allied health science majors who are working on a data science minor. The course assumes one prior statistics course, and is taught using R. Benefits to students include: learning how to read primary research papers in their discipline (or, for statistics majors, a discipline of interest) for effect size statistics and other quantitative concepts that are important for data synthesis, connecting with issues around research reproducibility and credibility in their discipline, and conducting an original meta-analysis in a research literature of interest

Responses to Weight Loss Treatment Among Obese Individuals with and without BED: A Matched-study Meta-analysis

The moderating influence of binge eating status on obese individuals’ responses to weight loss treatment was evaluated with a meta-analysis of 36 tests of weight loss treatment (n=792) that were matched to control key background variables. After controlling for pre-treatment weight, treatment produced more weight loss in samples of obese non- BED compared with obese BED participants. Weight loss treatment produced large posttreatment reductions in depression in both obese BED and non-BED samples. The results indicate that BED status moderated post-treatment weight loss among people in weight treatment programs. Obese BED (average weight loss= 1.3 kg) samples lost negligible weight compared to obese non-BED (average weight loss= 10.5 kg) samples. BED status did not moderate psychological responses to treatment: both BED and non-BED samples experienced large post-treatment reductions in depression. The clinical implications of these findings are discussed

Obesity, Self-Complexity, and Compartmentalization: On the Implications of Obesity for Self-Concept Organization

The relationship between obesity and structural aspects of the self-concept was examined in adult women. Participants were 119 adult women [age range: 18-73, M=26.9; body mass index (BMI) range: 16.2-54.7, M=27.3] who completed measures of self-esteem, self-complexity, and the spontaneous self-concept. BMI was associated with less complex and more compartmentalized self-knowledge and more frequent mention of weight-stereotypic traits as self-descriptive. The findings are discussed in the context of research on obesity- related stigma

Reaching out to behavioral science audiences via meta-analysis

Exposing students to meta-analysis supports ASA Curriculum Guidelines regarding the importance of data science, working with real and unusual data, diverse approaches to statistical models, and building relationships with allied disciplines. This poster present some of the benefits to students from taking an undergraduate meta-analysis course. The course is a 300-level statistics elective (class size: 12-18) that enrolls statistics majors and students from social and allied health science majors who are working on a data science minor. The course assumes one prior statistics course, and is taught using R. Benefits to students include: learning how to read primary research papers in their discipline (or, for statistics majors, a discipline of interest) for effect size statistics and other quantitative concepts that are important for data synthesis, connecting with issues around research reproducibility and credibility in their discipline, and conducting an original meta-analysis in a research literature of interest

Understanding the Psychology of Diversity

Covering the cognitive and emotional foundations of prejudice underpinning all forms of inequality, Understanding the Psychology of Diversity examines social difference, social inequality, and the problems inherent to inequality from a psychological perspective. By studying how the individual constructs his or her view of social diversity and how she or he is defined and influenced by social diversity, the author presents all that psychology has to offer on this critically important topic.https://fisherpub.sjfc.edu/bookshelf/1013/thumbnail.jp