scholarly journals Use of Quantitative Morphological Analysis Combined with a Large Sample Size for Estimating Morphological Variability in a Case Study of Armoured Mite Carabodes subarcticus Trägårdh, 1902 (Acari: Oribatida: Carabodidae) / Carabodes Subarcticus Trägårdh, 1902 (Acari: Oribatida: Carabodidae) Bruòçrèu Taksonomijâ Plaðâk Pielietoto Pazîmju Morfoloìijas Mainîbas Kvantitatîva Un Kvalitatîva Analîze Relatîvi Lielâ Paraugkopâ

2015 ◽  
Vol 69 (6) ◽  
pp. 314-325
Author(s):  
Uìis Kagainis
Keyword(s):  
Sample Size ◽  
Morphological Analysis ◽  
Local Population ◽  
Morphological Variability ◽  
Reference Population ◽  
Large Sample Size ◽  
Sample Sizes ◽  
Quantitative Characterisation ◽  
Species Descriptions ◽  
Larger Sample

AbstractThe morphology of Oribatida and similar little-known groups of organisms varies considerably, which complicates morphological analysis (e.g. species descriptions). Qualitative analyses have been carried out mostly on a small number of individuals (n < 25). There is lack of studies dealing with mechanisms of how that variation can change in relation to sample size and insufficient discussion on whether qualitative or quantitative analysis is more appropriate for description of morphological variability. A total of 500 adult Carabodes subarcticus Trägårdh, 1902 Oribatida were collected from a local population. Six qualitative and six quantitative traits were characterised using light microscopy and scanning electron microscopy. The relationships between the sample size of different subsamples (n < 500) and morphological variation were examined using randomised selection (10 000 replicates) and calculation of the percentage of cases in which the sizevalues were within a certain distance (less than 10%, 25%, or 50%) from the range of the reference population (n = 500). Qualitative traits were significantly less variable than quantitative due to binomial distribution of the obtained data; thus they were less comparable and interpretive to describe morphological variability. When sample size was small (n < 25), in less than 2 to 15% of cases the observed variability was within 10% distance of the range of the reference population. Larger sample sizes resulted in size-ranges that approached those of the reference population. It is possible that execution of quantitative characterisation and use of relatively larger sample sizes could improve species descriptions by characterising the morphological variability more precisely and objectively.

scholarly journals No one accelerometer-based physical activity data collection protocol can fit all research questions

2019 ◽  
Author(s):  
Patrick Bergman ◽  
Maria Hagströmer
Keyword(s):  
Physical Activity ◽  
Sample Size ◽  
Large Sample Size ◽  
Intensity Level ◽  
Sample Sizes ◽  
Activity Data ◽  
Convenience Sample ◽  
Mean Values ◽  
Repeated Observations ◽  
Measurement Protocol

Abstract BACKGROUND Measuring physical activity and sedentary behavior accurately remains a challenge. When describing the uncertainty of mean values or when making group comparisons, minimising Standard Error of the Mean (SEM) is important. The sample size and the number of repeated observations within each subject influence the size of the SEM. In this study we have investigated how different combinations of sample sizes and repeated observations influence the magnitude of the SEM. METHODS A convenience sample were asked to wear an accelerometer for 28 consecutive days. Based on the within and between subject variances the SEM for the different combinations of sample sizes and number of monitored days was calculated. RESULTS Fifty subjects (67% women, mean±SD age 41±19 years) were included. The analyses showed, independent of which intensity level of physical activity or how measurement protocol was designed, that the largest reductions in SEM was seen as the sample size were increased. The same magnitude in reductions to SEM was not seen for increasing the number of repeated measurement days within each subject. CONCLUSION The most effective way of reducing the SEM is to have a large sample size rather than a long observation period within each individual. Even though the importance of reducing the SEM to increase the power of detecting differences between groups is well-known it is seldom considered when developing appropriate protocols for accelerometer based research. Therefore the results presented herein serves to highlight this fact and have the potential to stimulate debate and challenge current best practice recommendations of accelerometer based physical activity research.

A Longitudinal Field Investigation of Auditor Risk Assessments and Sample Size Decisions

2003 ◽  
Vol 78 (4) ◽  
pp. 983-1002 ◽  
Author(s):  
Randal J. Elder ◽  
Robert D. Allen
Keyword(s):  
Sample Size ◽  
Field Investigation ◽  
Risk Assessments ◽  
Sample Sizes ◽  
Accounting Firms ◽  
Inherent Risk ◽  
Changes In Risk ◽  
Larger Sample ◽  
Average Sample ◽  
Do So

This study examines changes in auditor risk assessments and sample size decisions based on information gathered from three large accounting firms for audits during 1994 and 1999. The five-year interval between data collection periods allows us to measure changes in risk assessments and sample sizes between the two periods. Auditors relied on controls and assessed inherent risk below the maximum on most audits, and were more likely to do so in the later period, consistent with a trend of lower risk assessment levels. Average sample sizes declined between 1994 and 1999 for the firms that had larger sample sizes in the earlier period. Overall, we find a significant relationship between inherent risk assessments and sample sizes, but this relationship is stronger in the earlier period and is not significant for all firms, especially in the later period. We find limited evidence of a relationship between control risk and sample sizes.

scholarly journals Sample Size to Estimate the Mean and Median of Traits in Canola

2018 ◽  
Vol 10 (11) ◽  
pp. 123
Author(s):  
Alberto Cargnelutti Filho ◽  
Cleiton Antonio Wartha ◽  
Jéssica Andiara Kleinpaul ◽  
Ismael Mario Marcio Neu ◽  
Daniela Lixinski Silveira
Keyword(s):  
Sample Size ◽  
Aerial Part ◽  
Dry Matter ◽  
Central Tendency ◽  
Sample Sizes ◽  
Brassica Napus L ◽  
Fresh Matter ◽  
The Mean ◽  
Productive Traits ◽  
Larger Sample

The aim of this study was to determine the sample size (i.e., number of plants) required to estimate the mean and median of canola (Brassica napus L.) traits of the Hyola 61, Hyola 76, and Hyola 433 hybrids with precision levels. At 124 days after sowing, 225 plants of each hybrid were randomly collected. In each plant, morphological (plant height) and productive traits (number of siliques, fresh matter of siliques, fresh matter of aerial part without siliques, fresh matter of aerial part, dry matter of siliques, dry matter of aerial part without siliques, and dry matter of aerial part) were measured. For each trait, measures of central tendency, variability, skewness, and kurtosis were calculated. Sample size was determined by resampling with replacement of 10,000 resamples. The sample size required for the estimation of measures of central tendency (mean and median) varies between traits and hybrids. Productive traits required larger sample sizes in relation to the morphological traits. Larger sample sizes are required for the hybrids Hyola 433, Hyola 61, and Hyola 76, in this sequence. In order to estimate the mean of canola traits of the Hyola 61, Hyola 76 e Hyola 433 hybrids with the amplitude of the confidence interval of 95% equal to 30% of the estimated mean, 208 plants are required. Whereas 661 plants are necessary to estimate the median with the same precision.

scholarly journals Tools for Ergonomic Intervention: The Development and Analysis of a Cost Calculator

2020 ◽  
Vol 64 (1) ◽  
pp. 465-469
Author(s):  
Derek Stephens ◽  
Diana J. Schwerha
Keyword(s):  
Decision Making ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Quality Data ◽  
Sample Sizes ◽  
System Usability ◽  
The Mean ◽  
Larger Sample ◽  
Ergonomic Intervention

The purpose of this study was to determine if safety professionals can use an ergonomic intervention costing calculator, which integrates performance and quality data into the costing matrix, to increase communication and better of decision making for the company. The sample size included 9 participants, which included four safety managers, four EHS managers, and one HR generalist. Results showed that all participants found the calculator very useful, well integrated, and it increased communication across the company. The mean System Usability Score (SUS) score was 82, which is rated as a perfectly acceptable software for use. Recommendations from this study include adding some additional features to the calculator, increasing awareness and availability of the calculator, and conducting further analysis using larger sample sizes. Limitations in this study include small sample size and limited interventions that were tested.

scholarly journals No one accelerometer-based physical activity data collection protocol can fit all research questions

2019 ◽  
Author(s):  
Patrick Bergman ◽  
Maria Hagströmer
Keyword(s):  
Physical Activity ◽  
Sample Size ◽  
Large Sample Size ◽  
Intensity Level ◽  
Sample Sizes ◽  
Activity Data ◽  
Convenience Sample ◽  
Mean Values ◽  
Repeated Observations ◽  
Measurement Protocol

Abstract BACKGROUND Measuring physical activity and sedentary behavior accurately remains a challenge. When describing the uncertainty of mean values or when making group comparisons, minimising Standard Error of the Mean (SEM) is important. The sample size and the number of repeated observations within each subject influence the size of the SEM. In this study we have investigated how different combinations of sample sizes and repeated observations influence the magnitude of the SEM. METHODS A convenience sample were asked to wear an accelerometer for 28 consecutive days. Based on the within and between subject variances the SEM for the different combinations of sample sizes and number of monitored days was calculated. RESULTS Fifty subjects (67% women, mean±SD age 41±19 years) were included. The analyses showed, independent of which intensity level of physical activity or how measurement protocol was designed, that the largest reductions in SEM was seen as the sample size were increased. The same magnitude in reductions to SEM was not seen for increasing the number of repeated measurement days within each subject. CONCLUSION The most effective way of reducing the SEM is to have a large sample size rather than a long observation period within each individual. Even though the importance of reducing the SEM to increase the power of detecting differences between groups is well-known it is seldom considered when developing appropriate protocols for accelerometer based research. Therefore the results presented herein serves to highlight this fact and have the potential to stimulate debate and challenge current best practice recommendations of accelerometer based physical activity research.

scholarly journals No one accelerometer-based physical activity data collection protocol can fit all research questions

2020 ◽  
Author(s):  
Patrick Bergman ◽  
Maria Hagströmer
Keyword(s):  
Physical Activity ◽  
Sample Size ◽  
Large Sample Size ◽  
Intensity Level ◽  
Sample Sizes ◽  
Activity Data ◽  
Convenience Sample ◽  
Mean Values ◽  
Repeated Observations ◽  
Measurement Protocol

Abstract BACKGROUND Measuring physical activity and sedentary behavior accurately remains a challenge. When describing the uncertainty of mean values or when making group comparisons, minimising Standard Error of the Mean (SEM) is important. The sample size and the number of repeated observations within each subject influence the size of the SEM. In this study we have investigated how different combinations of sample sizes and repeated observations influence the magnitude of the SEM. METHODS A convenience sample were asked to wear an accelerometer for 28 consecutive days. Based on the within and between subject variances the SEM for the different combinations of sample sizes and number of monitored days was calculated. RESULTS Fifty subjects (67% women, mean±SD age 41±19 years) were included. The analyses showed, independent of which intensity level of physical activity or how measurement protocol was designed, that the largest reductions in SEM was seen as the sample size were increased. The same magnitude in reductions to SEM was not seen for increasing the number of repeated measurement days within each subject. CONCLUSION The most effective way of reducing the SEM is to have a large sample size rather than a long observation period within each individual. Even though the importance of reducing the SEM to increase the power of detecting differences between groups is well-known it is seldom considered when developing appropriate protocols for accelerometer based research. Therefore the results presented herein serves to highlight this fact and have the potential to stimulate debate and challenge current best practice recommendations of accelerometer based physical activity research.

The Effectiveness of Increasing Sample Size to Mitigate the Influence of Population Characteristics in Haphazard Sampling

2001 ◽  
Vol 20 (1) ◽  
pp. 169-185 ◽  
Author(s):  
Thomas W. Hall ◽  
Terri L. Herron ◽  
Bethane Jo Pierce ◽  
Terry J. Witt
Keyword(s):  
Sample Size ◽  
Population Characteristics ◽  
Audit Tool ◽  
Sample Sizes ◽  
Cast Doubt ◽  
Audit Sampling ◽  
Physical Features ◽  
Over 40 Years ◽  
Two Populations ◽  
Larger Sample

Over 40 years ago both Deming (1954) and Arkin (1957) expressed concerns that the composition of samples chosen through haphazard selection may be unrepresentative due to the presence of unintended selection biases. To mitigate this problem some experts in the field of audit sampling recommend increasing sample sizes by up to 100 percent when utilizing haphazard selection. To examine the effectiveness of this recommendation 142 participants selected haphazard samples from two populations. The compositions of these samples were then analyzed to determine if certain population elements were overrepresented, and if the extent of overrepresentation declined as sample size increased. Analyses disclosed that certain population elements were overrepresented in the samples. Also, increasing sample size produced no statistically significant change in the composition of samples from one population, while in the second population increasing the sample size produced a statistically significant but minor reduction in overrepresentation. These results suggest that individuals may be incapable of complying with audit guidelines that haphazard sample selections be made without regard to the observable physical features of population elements and cast doubt on the effectiveness of using larger sample sizes to mitigate the problem. Given these findings, standard-setting bodies should reconsider the conditions under which haphazard sampling is sanctioned as a reliable audit tool.

scholarly journals How sample size influences the replicability of task-based fMRI

2017 ◽  
Author(s):  
Benjamin O. Turner ◽  
Erick J. Paul ◽  
Michael B. Miller ◽  
Aron K. Barbey
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Large Range ◽  
Test Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Typical Sample ◽  
The Impact ◽  
Larger Sample ◽  
Different Levels

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven distinct tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

Planning the Size of Clinical Trials with Allowance for Patient Noncompliance

1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Event Rate ◽  
Survival Functions ◽  
Time Intervals ◽  
Patient Noncompliance ◽  
Event Rates ◽  
Larger Sample

AbstractVarious approaches are considered for adjustment of clinical trial size for patient noncompliance. Such approaches either model the effect of noncompliance through comparison of two survival distributions or two simple proportions. Models that allow for variation of noncompliance and event rates between time intervals are also considered. The approach that models the noncompliance adjustment on the basis of survival functions is conservative and hence requires larger sample size. The model to be selected for noncompliance adjustment depends upon available estimates of noncompliance and event rate patterns.

Augmentation Strategies for Clozapine-Resistant Patients with Schizophrenia

2020 ◽  
Vol 26 (2) ◽  
pp. 218-227
Author(s):  
Yi-Hang Chiu ◽  
Chia-Yueh Hsu ◽  
Mong-Liang Lu ◽  
Chun-Hsin Chen
Keyword(s):  
Sample Size ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Mood Stabilizers ◽  
Treatment Resistant ◽  
Double Blind ◽  
Beneficial Effects ◽  
Augmentation Strategies ◽  
Pubmed Database ◽  
Augmentation Strategy ◽  
Larger Sample

Background: Clozapine has been used in treatment-resistant patients with schizophrenia. However, only 40% of patients with treatment-resistant schizophrenia have response to clozapine. Many augmentation strategies have been proposed to treat those clozapine-resistant patients, but the results are inconclusive. In this review, we intended to review papers dealing with the augmentation strategies in the treatment of clozapineresistant patients with schizophrenia. Method: We reviewed randomized, double-blind, placebo- or sham-controlled trials (RCT) for clozapine-resistant patients with schizophrenia in Embase, PsycINFO, Cochrane, and PubMed database from January 1990 to June 2019. Results: Antipsychotics, antidepressants, mood stabilizers, brain stimulation, such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation, and other strategies, were used as an augmentation in clozapine-resistant patients with schizophrenia. Except for better evidence in memantine with 2 RCTs and cognitive behavior therapy in 2 studies to support its effectiveness, we found that all the other effective augmentations, including sulpiride, ziprasidone, duloxetine, mirtazapine, ECT, sodium benzoate, ginkgo biloba, and minocycline, had only one RCT with limited sample size. Conclusion: In this review, no definite effective augmentation strategy was found for clozapine-resistant patients. Some potential strategies with beneficial effects on psychopathology need further studies with a larger sample size to support their efficacy.

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