Consumer Perceptions of Advertising as Misleading
A mail survey was conducted of 314 residents of two California cities to measure consumer perception of the prevalence of misleading advertising. Information was obtained concerning the extent of misleading advertising in the various media, for 30 products and services and for three age groups. The findings indicate that over half of the sample viewed “most” or “all” mail and telephone advertising as misleading, and that 38 percent of the respondents regarded “most” or “all” television advertising as misleading. Newspaper advertising was considered the most credible. Findings indicate that “most” or “all” advertising for products and services was viewed as misleading by 30 percent of the respondents. Findings for actions taken concerning misleading advertising indicate that nearly half of the sample had registered a complaint to the store or person advertising and that 29 percent of the respondents had complained to a manufacturer. Also, findings for the age groups indicate that two out of three respondents regarded “most” or “all” advertising directed at children as seriously misleading. Advertising directed at senior citizens was viewed as seriously misleading by 45 percent of the sample, and advertising directed at young and middle-aged adults was viewed as only slightly less misleading.
An Analysis of Health-Related Advertising in Spanish Radio
Resumen en: The two main objectives of this study are to identify misleading claims in advertising of health-related products and quantify the presence of messagesth...
Distribution-free monitoring of univariate processes
We consider statistical process control (SPC) of univariate processes when observed data are not normally distributed. Most existing SPC procedures are based on the normality assumption. In the literature, it has been demonstrated that their performance is unreliable in cases when they are used for monitoring non-normal processes. To overcome this limitation, we propose two SPC control charts for applications when the process data are not normal, and compare them with the traditional CUSUM chart and two recent distribution-free control charts. Some empirical guidelines are provided for practitioners to choose a proper control chart for a specific application with non-normal data.