Background Little is known about the general population prevalence or severity of DSM-IV mental disorders. Objective To estimate 12-month prevalence, severity, and comorbidity of DSM-IV anxiety, mood, impulse control, and substance disorders in the recently completed US National Comorbidity Survey Replication. Design and Setting Nationally representative face-to-face household survey conducted between February 2001 and April 2003 using a fully structured diagnostic interview, the World Health Organization World Mental Health Survey Initiative version of the Composite International Diagnostic Interview. Participants Nine thousand two hundred eighty-two English-speaking respondents 18 years and older. Main Outcome Measures Twelve-month DSM-IV disorders. Results Twelve-month prevalence estimates were anxiety, 18.1%; mood, 9.5%; impulse control, 8.9%; substance, 3.8%; and any disorder, 26.2%. Of 12-month cases, 22.3% were classified as serious; 37.3%, moderate; and 40.4%, mild. Fifty-five percent carried only a single diagnosis; 22%, 2 diagnoses; and 23%, 3 or more diagnoses. Latent class analysis detected 7 multivariate disorder classes, including 3 highly comorbid classes representing 7% of the population. Conclusion Although mental disorders are widespread, serious cases are concentrated among a relatively small proportion of cases with high comorbidity.

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Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication

A theoretical model of psychological well-being that encompasses 6 distinct dimensions of wellness (Autonomy, Environmental Mastery, Personal Growth, Positive Relations with Others, Purpose in Life, Self-Acceptance) was tested with data from a nationally representative sample of adults (N = 1,108), aged 25 and older, who participated in telephone interviews. Confirmatory factor analyses provided support for the proposed 6-factor model, with a single second-order super factor. The model was superior in fit over single-factor and other artifactual models. Age and sex differences on the various well-being dimensions replicated prior findings. Comparisons with other frequently used indicators (positive and negative affect, life satisfaction) demonstrated that the latter neglect key aspects of positive functioning emphasized in theories of health and well-being.

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The structure of psychological well-being revisited

Statistical methods in cancer research

Background Many people use unconventional therapies for health problems, but the extent of this use and the costs are not known. We conducted a national survey to determine the prevalence, costs, and patterns of use of unconventional therapies, such as acupuncture and chiropractic. Methods We limited the therapies studied to 16 commonly used interventions neither taught widely in U.S. medical schools nor generally available in U.S. hospitals. We completed telephone interviews with 1539 adults (response rate, 67 percent) in a national sample of adults 18 years of age or older in 1990. We asked respondents to report any serious or bothersome medical conditions and details of their use of conventional medical services; we then inquired about their use of unconventional therapy. Results One in three respondents (34 percent) reported using at least one unconventional therapy in the past year, and a third of these saw providers for unconventional therapy. The latter group had made an average of 19 visits to suc...

Unconventional medicine in the United States. Prevalence, costs, and patterns of use.

Ethnic diversity is increasing in most advanced countries, driven mostly by sharp increases in immigration. In the long run immigration and diversity are likely to have important cultural, economic, fiscal, and developmental benefits. In the short run, however, immigration and ethnic diversity tend to reduce social solidarity and social capital. New evidence from the US suggests that in ethnically diverse neighbourhoods residents of all races tend to ‘hunker down’. Trust (even of one's own race) is lower, altruism and community cooperation rarer, friends fewer. In the long run, however, successful immigrant societies have overcome such fragmentation by creating new, cross-cutting forms of social solidarity and more encompassing identities. Illustrations of becoming comfortable with diversity are drawn from the US military, religious institutions, and earlier waves of American immigration.

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E Pluribus Unum: Diversity and Community in the Twenty‐first Century The 2006 Johan Skytte Prize Lecture

Many surveys of the U.S. household population are experiencing higher refusal rates. Nonresponse can, but need not, induce nonresponse bias in survey estimates. Recent empirical findings illustrate cases when the linkage between nonresponse rates and nonresponse biases is absent. Despite this, professional standards continue to urge high response rates. Statistical expressions of nonresponse bias can be translated into causal models to guide hypotheses about when nonresponse. causes bias. Alternative designs to measure nonresponse bias exist, providing different but incomplete information about the nature of the bias. A synthesis of research studies estimating nonresponse bias shows the bias often present. A logical question at this moment in history is what advantage probability sample surveys have if they suffer from high nonresponse rates. Since postsurvey adjustment for nonresponse requires auxiliary variables, the answer depends on the nature of the design and the quality of the auxiliary variables.

Nonresponse Rates and Nonresponse Bias in Household Surveys

1. An Introduction to Survey Errors.1.1 Diverse Perspectives on Survey Research.1.2 The Themes of Integration: Errors and Costs.1.3 The Languages of Error.1.4 Classifications of Error Within Survey Statistics.1.5 Terminology of Errors in Psychological Measurement.1.6 The Language of Errors in Econometrics.1.7 Debates About Inferential Errors.1.8 Important features of language Differences.1.9 Summary: The Tyranny of the Measurable.1.10 Summary and Plan of This Book.2. An Introduction to Survey Costs.2.1 Rationale for a Joint Concern About Costs and Errors.2.2 Use of Cost and Error Models in Sample Design.2.3 Criticisms of Cost-Error Modeling to Guide Survey Decisions.2.4 Nonlinear Cost Models Often Apply to Practical Survey Administration.2.5 Survey Cost Models are Inherently Discontinuous.2.6 Cost Models Often Have Stochastic Features.2.7 Domains of Applicability of Cost Models Must Be Specified.2.8 Simulation Studies Might Be Best Suited to Design Decisions.2.9 Is Time Money?2.10 Summary: Cost Models and Survey Errors.3. Costs and Errors of Covering the Population.3.1 Definitions of Populations Relevant to the Survey.3.2 Coverage Error in Descriptive Statistics.3.3 An Approach to Coverage Error in Analytic Statistics.3.4 Components of Coverage Error.3.5 Coverage Problems with the Target Population of Households.3.6 Measurement of and Adjustments for Noncoverage Error.3.7 Survey Cost Issues Involving Coverage Error.3.8 Summary.4. Nonresponse in Sample Surveys.4.1 Nonresponse Rates.4.2 Response Rate Calculation.4.3 Temporal Change in Response Rates.4.4 Item Missing Data.4.5 Statistical Treatment of Nonresponse in Surveys.4.6 Summary.5. Probing the Causes of Nonresponse and Efforts to Reduce Nonresponse.5.1 Empricial Correlates of Survey Participation.5.2 Efforts by Survey Methodologists to Reduce Refusals.5.3 Sociological Concepts Relevant to Survey Nonresponse.5.4 Psychological Attributes of Nonrespondents.5.5 Summary.6. Costs and Errors Arising from Sampling.6.1 Introduction.6.2 The nature of Sampling Error.6.3 Measuring Sampling Error.6.4 Four Effects of the Design on Sampling Error.6.5 The Effect of Nonsampling Errors on Sampling Error Estimates.6.6 Measuring Sampling Errors on Sample Means and Proportions from Complex Samples.6.7 The Debate on Reflecting the Sample Design in Estimating Analytic Statistics.6.8 Reflecting the Sample Design When Estimating Complex Statistics.6.9 Summary.7. Empirical Estimation of Survey Measurement Error.7.1 A First Estimation of Observational Errors Versus Errors of Nonobservation.7.2 Laboratory Experiments Resembling the Survey Interview.7.3 Measures External to the Survey.7.4 Randomized Assignment of Measurement Procedures to Sample Persons.7.5 Repeated measurements of the Same Persons.7.6 Summary of Individual Techniques of Measurement Errors Estimation.7.7 Combinations of Design Features.7.8 Summary.8. The Interviewer as a Source of Survey Measurement Error.8.1 Alternative Views on the Role of the Observer.8.2 The Roles of the Survey Interviewer.8.3 Designs for Measuring Interview Variance.8.4 Interviewer Effects in Personal Interview Surveys.8.5 Interviewer Effects in Centralized Telephone Surveys.8.6 Explaining the Magnitude of Interviewer Effects.8.7 Summary of Research on Interviewer Variance.8.8 Measurement of Interviewer Compliance With Training Guidelines.8.9 Experiments in Manipulating Interviewer Behavior.8.10 Social Psychological and Sociological Explanations for Response Error Associated with Interviewers.8.11 Summary.9. The Respondent as a Source of Measurement Error.9.1 Components of Response Formation Process.9.2 The Encoding Process and the Absence of Knowledge Relevant to the Survey Question.9.3 Comprehension of the Survey Question.9.4 Retrieval of Information from Memory.9.5 Judgment of Appropriate Answer.9.6 Communication of Response.9.7 Sociological and Demographic Correlates of Respondent Error.9.8 Summary.10. Measurement Errors Associated with the Questionnaire.101 Properties of Words in Questions.10.2 Properties of Question Structure.10.3 Properties of Question Order.10.4 Conclusions About Measurement Error Related to the Questionnaire.10.5 Estimates of Measurement Error from Multiple Indicator Models.10.6 Cost Models Associated with Measurement Error Through the Questionnaire.10.7 Summary.11. Response Effects of the Mode of Data Collection.11.1 Two Very Different Questions About Mode of Data Collection.11.2 Properties of Media of Communication.11.3 Applying Theories of Mediated Communication to Survey Research.11.4 Findings from the Telephone Survey Literature.11.5 Interaction Effects of Mode and Other Design.11.6 Survey Costs and the Mode of Data Collection.11.7 Cost and Error Modeling with Telephone and Face to Face Surveys.11.8 Summary.References.Index.

Survey errors and survey costs

Fifty-nine methodological studies were designed to esti- mate the magnitude of nonresponse bias in statistics of interest. These studies use a variety of designs: sampling frames with rich variables, data from administrative records matched to sample case, use of screening- interview data to describe nonrespondents to main interviews, followup of nonrespondents to initial phases of field effort, and measures of be- havior intentions to respond to a survey. This permits exploration of which circumstances produce a relationship between nonresponse rates and nonresponse bias and which, do not. The predictors are design fea- tures of the surveys, characteristics of the sample, and attributes of the survey statistics computed in the surveys.

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The Impact of Nonresponse Rates on Nonresponse Bias A Meta-Analysis

An Introduction to Survey Participation. A Conceptual Framework for Survey Participation. Data Resources for Testing Theories of Survey Participation. Influences on the Likelihood of Contact. Influences of Household Characteristics on Survey Cooperation. Social Environmental Influences on Survey Participation. Influences of the Interviewers. When Interviewers Meet Householders: The Nature of Initial Interactions. Influences of Householder-Interviewer Interactions on Survey Cooperation. How Survey Design Features Affect Participation. Practical Survey Design Acknowledging Nonresponse. References. Index.

Nonresponse in household interview surveys

Critics of public opinion polls often claim that methodological shortcuts taken to collect timely data produce biased results This study compares two random digit dial national telephone surveys that used identical questionnaires but very different levels of effort: a "Standard" survey conducted over a 5-day period that used a sample of adults who were home when the interviewer called, and a "Rigorous" survey conducted over an 8-week period that used random selection from among all adult household members Response rates, computed according to AAPOR guidelines, were 606 percent for the Rigorous and 360 percent for the Standard study Nonetheless, the two surveys produced similar results Across 91 comparisons, no difference exceeded 9 percentage points, and the average difference was about 2 percentage points Most of the statistically significant differences were among demographic items Very few significant differences were found on attention to media and engagement in politics, social trust and connectedness, and most social and political attitudes, including even those toward surveys

Robert M. Groves

Papers

Nonresponse Rates and Nonresponse Bias in Household Surveys

Survey errors and survey costs

The Impact of Nonresponse Rates on Nonresponse Bias A Meta-Analysis

Nonresponse in household interview surveys

Consequences of Reducing Nonresponse in a National Telephone Survey