Effect Of Sampling Error Research Articles

Comparative evolutionary genetics of trematode parasites (Plagiorchiidae) and their anuran hosts

A study of five trematode species of the genera Glypthelmins and Haplometrana inhabiting anurans produced allozyme-based estimates of Nei's (1978) genetic distance ranging from 0.126 to 1.867. An estimate of trematode phylogeny is in agreement with earlier morphologically based phylogenetic hypotheses and is similar to the phylogeny for associated host ranid and hylid frogs based on rDNA and morphology. A comparison of the genetic distances observed among trematodes with those of their respective hosts suggests that host and parasite genetic distances are not significantly correlated. This is counter to expectations based on a molecular clock for protein evolution in hosts and parasites. These results may reflect (i) the effects of sampling error on genetic distance estimates, (ii) a non-clocklike rate of protein evolution in hosts and (or) parasites, (iii) temporally independent speciation events in associated host and parasite lineages, or (iv) the effects of host hybridization.

The Effects of Sampling Error on Detection Limits Determined for Quantitative X-Ray Diffraction

AbstractThis paper investigates the contributions of sample preparation to the analytical lower limits of detection and errors for the quantitative X-ray diffraction analysis of a simple, binary system of quartz and calcite. This study further demonstrates the danger of using only statistical and instrumental contributions to detection limits and analytical errors.

Subsample and jackknife: A general technique for the estimation of sampling errors, with applications and examples in the field of transport planning

A method of estimating the effect of sampling error on derived results is described. The technique involves the division of the full sample into overlapping subsamples and the subsequent parallel processing of these subsamples. The particular advantage of the technique is the way in which it can be used to follow automatically the effect of sampling variance through the most complex operations, such as the calibration of a transport model.

The Effects of Item and Examinee Sampling in the Analysis and Selection of Objective Test Items

Abstract The investigation sets out to determine whether in the construction of normreferenced tests the effects of sampling errors in the pre‐testing procedure outweigh the theoretical advantages to be gained in selecting items with the highest discrimination indices. The procedure for pre‐testing and selecting the items is simulated by sampling artificial matrices each representing the scores of a population of examinees on a domain of items. The results indicate that the sampling errors do not have a significant deleterious effect if the samples in the pre‐testing procedure contain more than 50 items and 25 examinees. Moreover, there may be very little to be gained by using larger samples.

A note on the effects of sampling errors on the accuracy of genetic selection indices

A note on the effects of sampling errors on the accuracy of genetic selection indices

Aspects of the inheritance of intellectual abilities.

Genetic and environmental influences on IQ were examined in various subgroups of the Texas Adoption Project sample, including subdivisions by age, sex, and socioeconomic status. Also investigated were the effects of sampling error, restriction of range, ureliability of measures,whether or not the adoptive parents had information about the biological mother, whether or not the biological mother had elevated MMPI scores, and differences between verbal and performance abilities. The principal method used was path analysis, and the heritabilities obtained fell mostly in the range of 0.30 to 0.65.

Objective analysis of air pollution monitoring network data; spatial interpolation and network density

To obtain meaningful interpretations of the results of fixed air pollution monitoring stations, quantitative information on the spatial representativity of these results is of crucial importance. On the basis of data of the Dutch Air Pollution Monitoring Network a study was undertaken to estimate the absolute and relative errors which are involved in extrapolating the results beyond the actual spatial argument of measurement. In the first place, three interpolation techniques, optimum interpolation, eigenvector interpolation and so called distance-density interpolation, were compared for SO 2, NO, NO 2 and O 3. The differences between the results of these techniques proved to be small. It was further concluded that the interpolation errors and the associated persistence in space and time, as given by mutual correlations, should be specified with respect to pure space- or space-time variability. In the second place, the interpolation errors were generalized to other network densities on the basis of the optimum interpolation scheme and empirical observations. From the resulting relation between interpolation error and network density it was concluded that the SO 2-network of 108 stations over the measurement area of 150 × 220 km 2 results in relative errors of 20 %, small enough to detect mesoscale transports downwind of major source areas. The effect of sampling error, i.e., the small scale influences of local sources combined with the effect of measurement error, appeared to be of overriding importance in the efficiency of reconstruction of pollutant concentration fields at a given confidence level from monitoring network data.

Influence of Benthos Life History upon the Estimation of Secondary Production

Synchrony of cohorts is one of the life history features having most important effects upon the estimation of benthic secondary production, because most methods depend heavily upon recognition of discrete cohorts. The Hynes method, intended to circumvent the necessity of cohort distinction, still depends upon determination of trophic level, voltinism, minimum and maximum sizes, and length of aquatic life. Knowledge of preferred habitat, distribution, and behavior are essential for accurate production estimates. Use of the production:mean standing stock (P/B) ratio (fairly constant at about 5 for cohort P/B of benthic invertebrates) to approximate production from standing stock data, also must account for trophic level, voltinism, and length of aquatic life. Various life history features are compared as to their probable effect on production estimation; in addition, they are compared to the effect of sampling errors. Key words: benthos, life history, production, secondary production, productivity

Effect of sampling errors on efficiency of selection indices. 2. Use of information on associated traits for improvement of a single important trait

SUMMARYAn analysis is undertaken of the effect of errors in estimates of parameters on the response to selection for an economically important trait (trait 1) when one or more additional traits are added in a selection index. The detailed analysis is confined to one additional trait (trait 2) which contributes useful information unless the genetic and phenotypic regressions of trait 1 on trait 2 are equal.If there are errors in parameter estimates the extra response obtained by including trait 2 will usually be over-predicted. When trait 2 actually contributes no useful information the predicted benefit equals the real loss in efficiency from its inclusion.The loss in efficiency from poor estimation of parameters, whether or not the second trait makes a contribution, is roughly one-quarter of the squared coefficient of variation of a heritability estimate of trait 1 in the same experiment.

Effect of sampling errors on efficiency of selection indices 1. Use of information from relatives for single trait improvement

SummaryAn analysis is undertaken of the effect of errors in estimates of parameters, particularly the intra-class correlations, on the response from selection for one trait using an index of individual together with full- and/or half-sib family records. A distinction is drawn between the response (R) possible with use of the optimum index, that predicted (R) and that achieved (R*) with an index which uses the sample estimates of the parameter values.It is found that the loss of efficiency (R*–R) using sample estimates is very small even for estimates far from the correct value. R is more sensitive to errors, particularly of the heritability and phenotypic variance estimates. Since the latter also appear in the prediction of response from individual selection, errors in predicting the relative responses from index and individual selection are small. Expected values of the proportional loss in response,are approximately proportional to the variance of the estimate of intra-class correlation. It is shown that in practice initial experiments with 20 or so families may be sufficient to get average proportional losses down to less than 1%.

A Computer Program for Investigating the Effects of Sampling Error on Multiple Regression Equations

A Computer Program for Investigating the Effects of Sampling Error on Multiple Regression Equations

Sampling error as a misleading artifact in “key factor analysis”

SummaryThe influences of sampling error in key factor analysis are investigated statistically. The error involved in the data distorts the results in various misleading ways. In the course of detecting key factors by correlation analysis, the distortion arises in the following two ways: (1) the contributions made by the first and the last components of population trend index (log I or K) to the total variation are overrated as compared with the others; and (2) spurious negative correlation arises between successive two components. The risk of misinterpretation due to such disturbance is usually increased further if the error is concentrated on any particular developmental stages. In the tests to detect density‐dependence by using regression analysis, the error consistently acts as if it were a density‐dependent factor: under the effect of sampling error, the slope b for the regression of log Ni+1 on log Ni, for example, is expected to become<1 even where there is no density‐dependent factor at all. A set of formulas are derived which may serve to check and correct these misleading distortions caused by the error. It is also shown that such undesirable influences can be avoided, at least to a considerable extent, if appropriate sampling plans are adopted for the study. The validity of key factor analysis is discussed in reference to this and some related problems.

Early Campanian Multituberculates (Mammalia: Allotheria) from the Upper Milk River Formation, Alberta

Mammalian teeth collected from the early Campanian Upper Milk River Formation, southernmost Alberta, document a hitherto unknown evolutionary radiation of Late Cretaceous ptilodontoid and taeniolabidoid multituberculates. New species of the ectypodontids Mesodma and Cimexomys, the ptilodontid Cimolodon, and the cimolomyid Meniscoessus are defined, and teeth possibly pertaining to a second species of Mesodma and two species of Cimolomys are described. A new genus and species with uncertain relationships to known multituberculate families, is tentatively classified in the Taeniolabidoidea. Among North American Late Cretaceous multituberculates, at least Mesodma, Cimexomys, Cimolodon, Cimolomys, and Meniscoessus are seen to be evolutionarily conservative during early Campanian to late Maestrichtian times. The effects of sampling error on relative taxonomic diversity at horizons in the North American Upper Cretaceous are discussed.

The Effect of Non-Sampling Errors on Measures of Association in 2 × 2 Contingency Tables

Abstract The effects of non-sampling errors on measures of association in 2 × 2 contingency tables are evaluated by the application of models due to the U. S. Bureau of the Census. This is achieved by first expressing the appropriate sample estimates in the form of Taylor series approximations involving cell probabilities, and then applying the model in a term by term fashion. In this way, the relative effects of sampling errors and response errors on the variability of an estimated measure of association may be interpreted in terms of a sampling variance component and a response variance component. Finally, some indication is given as to how re-survey information can be used to estimate these quantities.

Appendix 7: Effect of Sampling Errors on Vital Rates in Small Groups

Appendix 7: Effect of Sampling Errors on Vital Rates in Small Groups

THE STABILITY OF EQUATED TEST SCORES1

ABSTRACTThe effects of sampling error were studied empirically with respect to four methods of equating scales of tests administered to non‐overlapping groups of subjects: (1) mean and sigma method, (2) equi‐percentile method, (3) maximum likelihood method using an “anchor” test, (4) standard reference group method using an “anchor” test. The methods were compared under both random and stratified sampling. Results showed that sampling error was (1) smaller for those methods which make use of an “anchor” test than for those which do not, (2) smaller for equated scores closer to the mean of the total population than for those further from the mean, (3) was not decreased by stratification by institution when the “anchor” test methods of equating were used.

On Adjusting Sample Tabulations to Census Counts

T HERE are many occasions in which statisticians have to consider how they can make the best use of data that are obtained by sampling, particularly when they have available to them data from other sources that can be used to improve the estimates they wish to derive from the sample. Problems of this kind are likely to require the attention of statisticians to an increasing extent as the flow of information from sample surveys and sample tabulations drawn from established record systems expands. There are many indications of a strong trend in this direction. It promises to provide more data, more quickly, and estimates of greater value than the older sources of a more cumbersome or more casual nature. At the same time its value will depend on the fullest use of the data that are provided by other sources for the improvement of sample estimates and also for the supplementing of each by the other. An outstanding example of this problem is presented by the numerous tabulations of data from samples that are part of the 1950 Census of Population. In these the connection between the sample and the other source of data is very close since the sample is taken in connection with the regular enumeration or by a selection from the returns after they are completed. By these procedures the Bureau of the Census has greatly enriched the information it has been able to publish about the American population. The introduction of an element of sampling error into the results is much more than offset by the value of the additional variables and cross tabulations that sampling made possible. While the effect of sampling error may be a very reasonable price to pay for the additional data that sampling provides, a statistician very naturally tries to reduce the cost to a minimum. Moreover, he often finds it awkward to have two figures for the same item of information, one from the regular census count and the other from the sample estimate. He finds the public rather impatient in insisting that such figures be reconciled and that only the 'correct figure should be published.

THE STABILITY OF EQUATED TEST SCORES1

ABSTRACTThe effects of sampling error were studied empirically with respect to four methods of equating scales of tests administered to non‐overlapping groups of subjects: 1. mean and sigma method, 2. equi‐percentile method, 3. maximum likelihood method using an “anchor” test, 4. standard reference group method using an “anchor” test. The methods were compared under both random and stratified sampling. Results showed that sampling error was 1) smaller for those methods which make use of an anchor test than for those which do not, 2) smaller for equated scores closer to the mean of the total population than for those further from the mean, 3) was not decreased by stratification by institution when the “anchor” test methods of equating were used.

The role of correlated factors in factor analysis

The fundamental factor theorem is developed in matrix form for the case of correlated factors. The properties of the correlated factor system are discussed, and some effects of sampling error considered. The psychological meaning of correlated factors is discussed, and several mechanisms by which general factors may operate in the factorial system are indicated.

The relative importance of plot variation and of field and laboratory sampling errors in small plot pasture productivity experiments

An investigation of the relative importance in small plot pasture productivity experiments, of plot variations and of field and laboratory errors in sampling and subsampling for percentage dry matter and percentage nitrogen figures, revealed little advantage from duplicate sampling or subsampling in reducing the experimental error, which was due mainly to plot variation.The effect of sampling errors was of even less importance in the estimation of the actual yields of dry matter or of nitrogen.