Eta Squared,Partial Eta Squared,and Misreporting of Effect Size in Communication Research

Communication researchers, along with social scientists from a variety of disciplines, are increasingly recognizing the importance of reporting effect sizes to augment significance tests. Serious errors in the reporting of effect sizes, however, have appeared in recently published articles. This article calls for accurate reporting of estimates of effect size. Eta squared (η²) is the most commonly reported estimate of effect sized for the ANOVA. The classical formulation of eta squared (Pearson, 1911; Fisher, 1928) is distinguished from the lesser known partial eta squared (Cohen, 1973), and a mislabeling problem in the statistical software SPSS (1998) is identified. What SPSS reports as eta squared is really partial eta squared. Hence, researchers obtaining estimates of eta squared from SPSS are at risk of reporting incorrect values. Several simulations are reported to demonstrate critical issues. The strengths and limitations of several estimates of effect size used in ANOVA are discussed, as are the implications of the reporting errors. A list of suggestions for researchers is then offered.     

Patterns of sedentary behavior among adolescents.

OBJECTIVE: Reducing certain sedentary behaviors (e.g., watching television, using a computer) can be an effective weight loss strategy for youth. Knowledge about whether behaviors cluster together could inform interventions. STUDY DESIGN: Estimates of time spent in 6 sedentary behaviors (watching television, talking on the telephone, using a computer, listening to music, doing homework, reading) were cluster analyzed for a sample of 878 adolescents (52% girls, mean age = 12.7 years, 58% Caucasian). MAIN OUTCOME MEASURES: The clusters were based on the sedentary behaviors listed above and compared on environmental variables (e.g., household rules), psychosocial variables (e.g., self-efficacy, enjoyment), and health behaviors (e.g., physical activity, diet). RESULTS: Four clusters emerged: low sedentary, medium sedentary, selective high sedentary, and high sedentary. Analyses revealed significant cluster differences for gender (p < .002), age (p < .002), body mass index (p < .001), physical activity (p < .01), and fiber intake (p < .01). CONCLUSIONS: Results suggest a limited number of distinct sedentary behavior patterns. Further study is needed to determine how interventions may use cluster membership to target segments of the adolescent population.