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Observer error is ubiquitous in vegetation sampling. Observer error, along with other types of related nonsampling error, may result in species richness being artificially underestimated (i.e., false-negative errors) or artificially overestimated (i.e., false-positive errors). Because of the manner in which observer error is usually quantified, there exists a strong bias against the discovery of false positives. At least seven different types of nonsampling errors can occur when surveying vegetation species composition: overlooking, misidentification, cautious, mythical, anecdotal, transcription, and relocation. Six of these error types can result in false negatives and five can result in false positives. Another type of observer error that can occur in plant surveys is estimation error, which occurs when abundances are not accurately estimated. There are many potential underlying causes of nonsampling error. Humans observers, even when highly trained and experienced, are prone to commit errors through slips, lapses, and mistakes. A number of contributing factors of observer error have been identified, including characteristics associated with the vegetation, the environment, and the observers themselves; design-based flaws may also occur. Although it may not be possible to eliminate all sources of nonsampling error, most can be reduced through understanding the mechanisms underlying the various types of error, followed by training exercises and the consistent use of appropriate operating procedures. [ABSTRACT FROM AUTHOR]

It is well known among researchers and practitioners that election polls suffer from a variety of sampling and nonsampling errors, often collectively referred to as total survey error. Reported margins of error typically only capture sampling variability, and in particular, generally ignore nonsampling errors in defining the target population (e.g., errors due to uncertainty in who will vote). Here, we empirically analyze 4221 polls for 608 state-level presidential, senatorial, and gubernatorial elections between 1998 and 2014, all of which were conducted during the final three weeks of the campaigns. Comparing to the actual election outcomes, we find that average survey error as measured by root mean square error is approximately 3.5 percentage points, about twice as large as that implied by most reported margins of error. We decompose survey error into election-level bias and variance terms. We find that average absolute election-level bias is about 2 percentage points, indicating that polls for a given election often share a common component of error. This shared error may stem from the fact that polling organizations often face similar difficulties in reaching various subgroups of the population, and that they rely on similar screening rules when estimating who will vote. We also find that average election-level variance is higher than implied by simple random sampling, in part because polling organizations often use complex sampling designs and adjustment procedures. We conclude by discussing how these results help explain polling failures in the 2016 U.S. presidential election, and offer recommendations to improve polling practice. [ABSTRACT FROM AUTHOR]

Prevention of errors in survey data must always be among out highest ideals, but in such a complex process as a survey there are limits on what is achievable, because of cost, the absence of strong instruments for control or the emergence of unforeseen outcomes. Thus, effort must be devoted to identifying errors, remediating them, and designing better means of preventing or limiting there, where that is possible. Editing is typically a key instrument of identification and remediation. However, editing can consume very substantial resources and because the outcome is unlikely to be perfect, the very act itself introduces additional risks to data quality. For these reasons, it has been argued (e.g., de Waal [4]) that a selective approach to editing, focused as squarely as possible on the core analytical goal of a survey may be more appropriate than detailed review of all survey observations. For surveys supporting multiple uses, particularly ones involving multivariate analysis, there may be a need for a somewhat broader focus, but a more efficient approach may still be possible in such cases. This paper evaluates various approaches to selective editing, using various combinations of fully edited and unedited data from the 2010 Survey of Consumer Finances (SCF). The paper also explores the potential importance of contamination of the imputation process under selective editing. While editing has its direct effect on individual data items, it also alters the set of information used in imputing the missing values that result from the unwillingness or inability of respondents to provide answers or from the resetting of values to missing during the editing process. The results of the paper support a selective approach to editing and they indicate that any resulting contamination of imputation is relatively minor in the case of the SCF. [ABSTRACT FROM AUTHOR]

• Urban tree diameter measurements were more variable than from forest trees. • Measurement variability is related to the size of the attribute of interest. • Measurement variation adds trivially to uncertainty in ecosystem service estimates. • Formal field crew training and certification processes foster measurement consistency. The collection and analysis of urban forest inventory data has been steadily increasing in recent decades. In addition to typical assessments such as number of trees, size distribution, and species composition, estimates of ecosystem services provide empirical indicators of quantity and monetary value to anthropologic populations. As most urban inventories are sample-based, sources of uncertainty and their magnitude provide important information for judging the reliability of estimated population parameters. Most modern analysis tools provide an indication of uncertainty via a sampling error statistic, but other types of uncertainty due to measurements or statistical models are not accounted for. In this study, we examined measurement variation for a suite of urban tree attributes and found measurements were equally or less variable than those taken on forest-grown trees. The notable exception was tree diameter which was more highly variable. In addition to quantifying the measurement variability, simulations that propagate the variation were conducted to assess the additional variance incurred for estimates of ecosystem services and associated valuations. The results generally indicated an increase of about 1% or less in the standard error of the estimate for most ecosystem services and their value. Measurement variation may contribute larger amounts of uncertainty for urban inventories lacking adequate field crew training and quality assurance processes. [ABSTRACT FROM AUTHOR]