From 1979 to 1996, the Survey of Consumer Attitudes response rate remained roughly 70 percent. But number of calls to complete an interview and proportion of interviews requiring refusal conversion doubled. Using call-record histories, we explore what the consequences of lower response rates would have been if these additional efforts had not been undertaken. Both number of calls and initially cooperating (vs. initially refusing) are related to the Index of Consumer Sentiment (ICS), but only number of calls survives a control for demographic characteristics. We assess the impact of excluding respondents who required refusal conversion (which reduces the response rate 5-10 percentage points), respondents who required more than five calls to complete the interview (reducing the response rate about 25 percentage points), and those who required more than two calls (a reduction of about 50 percentage points). We found no effect of excluding any of these respondent groups on cross-sectional estimates of the ICS using monthly samples of hundreds of cases. For yearly estimates, based on thousands of cases, the exclusion of respondents who required more calls (though not of initial refusers) had an effect, but a very small one. One of the exclusions generally affected estimates of change over time in the ICS, irrespective of sample size.A basic tenet of survey research is that high response rates are better than low ones. Indeed, a low response rate is one of the few outcomes or features that-taken by itself-is considered a major threat to the usefulness of a survey.

The lengthy history and extended periods of relative design stability of the University of Michigan's Survey of Consumer Attitudes (SCA) make it an important resource for documenting response rate changes over the better part of survey research's history.

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The quantitative digital electroencephalogram (QEEG) was recorded from 19 scalp locations from 625 screened and evaluated normal individuals ranging in age from two months to 82 years. After editing to remove artifact, one-year to five-year groupings were selected to produce different average age groups. Estimates of gaussian distributions and logarithmic transforms of the digital EEG were used to establish approximate gaussian distributions when necessary for different Robert W. Thatcher is affiliated with the NeuroImaging Laboratory, Bay Pines VA variables and age groupings. The sensitivity of the lifespan database was determined by gaussian cross-validation for any selection of age range in which the average percentage of Z-scores ± 2 standard deviations (SD) equals approximately 2.3% and the average percentage for ± 3 SD equals approximately 0.13%. It was hypothesized that measures of gaussian cross-validation of Z-scores is a common metric by which the statistical sensitivity of any normative database for any age grouping can be calculated. This theory was tested by computing eyes-closed and eyes-open average reference and current source density norms and independently cross-validating and comparing to the linked ears norms. The results indicate that age-dependent digital EEG normative databases are reliable and stable and behave like different gaussian lenses that spatially focus the electroencephalogram. Clinical correlations of a normative database are determined by content validation and correlation with neuropsychological test scores and discriminate accuracy. Non-parametric statistics were presented as an important aid to establish the alpha level necessary to reject a hypothesis and to estimate Type I and Type II errors, especially when there are multiple comparisons of an individual's EEG to any normative EEG database.