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 Methodological Reporting Behavior, Sample Sizes, and Statistical Power in Studies of Event- Related Potentials: Barriers to Reproducibility and Replicability

2019 ◽  
Author(s):  
Peter E Clayson ◽  
Kaylie Amanda Carbine ◽  
Scott Baldwin ◽  
Michael J. Larson
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Event Related Potentials ◽  
Reporting Guidelines ◽  
Medium Effect ◽  
Sample Sizes ◽  
Reporting Behavior ◽  
Average Sample Size ◽  
Related Potentials ◽  
Average Sample

Methodological reporting guidelines for studies of event-related potentials (ERPs) were updated in Psychophysiology in 2014. These guidelines facilitate the communication of key methodological parameters (e.g., preprocessing steps). Failing to report key parameters represents a barrier to replication efforts, and difficultly with replicability increases in the presence of small sample sizes and low statistical power. We assessed whether guidelines are followed and estimated the average sample size and power in recent research. Reporting behavior, sample sizes, and statistical designs were coded for 150 randomly-sampled articles from five high-impact journals that frequently publish ERP research from 2011 to 2017. An average of 63% of guidelines were reported, and reporting behavior was similar across journals, suggesting that gaps in reporting is a shortcoming of the field rather than any specific journal. Publication of the guidelines paper had no impact on reporting behavior, suggesting that editors and peer reviewers are not enforcing these recommendations. The average sample size per group was 21. Statistical power was conservatively estimated as .72-.98 for a large effect size, .35-.73 for a medium effect, and .10-.18 for a small effect. These findings indicate that failing to report key guidelines is ubiquitous and that ERP studies are primarily powered to detect large effects. Such low power and insufficient following of reporting guidelines represent substantial barriers to replication efforts. The methodological transparency and replicability of studies can be improved by the open sharing of processing code and experimental tasks and by a priori sample size calculations to ensure adequately powered studies.

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 A Study on the X ¯ and S Control Charts with Unequal Sample Sizes

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 698
Author(s):  
Chanseok Park ◽  
Min Wang
Keyword(s):  
Sample Size ◽  
Control Charts ◽  
Weighted Average ◽  
Missing Observations ◽  
Population Parameters ◽  
Sample Sizes ◽  
Cost Constraints ◽  
The Mean ◽  
True Population ◽  
Average Sample

The control charts based on X ¯ and S are widely used to monitor the mean and variability of variables and can help quality engineers identify and investigate causes of the process variation. The usual requirement behind these control charts is that the sample sizes from the process are all equal, whereas this requirement may not be satisfied in practice due to missing observations, cost constraints, etc. To deal with this situation, several conventional methods were proposed. However, some methods based on weighted average approaches and an average sample size often result in degraded performance of the control charts because the adopted estimators are biased towards underestimating the true population parameters. These observations motivate us to investigate the existing methods with rigorous proofs and we provide a guideline to practitioners for the best selection to construct the X ¯ and S control charts when the sample sizes are not equal.

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 Learning from Cochrane systematic reviews: what improvements do these suggest for the design of trials?

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 178
Author(s):  
Stefania Pirosca ◽  
Mike Clarke ◽  
Shaun Treweek
Keyword(s):  
Sample Size ◽  
Systematic Reviews ◽  
Future Research ◽  
Randomised Trials ◽  
Review Group ◽  
The Central Nervous System ◽  
System Review ◽  
Larger Sample ◽  
Do So

Background: Many randomised trials have serious methodological flaws that fatally undermine their results, which makes the research wasteful. This is of concern for many, including those doing systematic reviews that include trials. Cochrane systematic reviews have a section called ‘ Implications for research’, which allows authors of the review to present their conclusions on how future research might be improved. Looking at these conclusions might highlight priority areas for improvement. Methods: We focused on the Cochrane Schizophrenia Review Group and the Multiple sclerosis and rare diseases of the central nervous system Review Group (the MS Review Group).  Reviews with citation dates between 2009 and 2019 were identified and the recommendations of review authors in ‘ Implications for research’ were put into categories. Results: Between 2009 and 2019 we identified 162 reviews for the Schizophrenia Review Group and 43 reviews for the MS Review Group. We created 22 categories of recommendations in total, of which 12 were common to both groups. The five most used categories were the same for both: better choice of outcomes; better choice of intervention/comparator; longer follow-up; larger sample size; use of validated scales.  Better choice of outcomes and/or intervention/comparator was recommended in over 50% of reviews. Longer follow-up and larger sample size were recommended in over a third, with use of validated scales being suggested in around a fifth of reviews. There was no obvious pattern of improvement over time for trials included in systematic reviews published by both groups. Conclusions: We suggest that trialists working in these and other areas ask themselves, or are compelled to do so by others (e.g. funders), why they have chosen their outcomes, intervention and comparator, whether follow-up is long enough, if the sample size is big enough and whether the scales they choose to measure their outcomes are appropriate.

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.

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.

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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