Single Sampling Plans for Inspection by Variables under a Variance Component Situation

2004 ◽  
pp. 43-57
Author(s):  
Peter-Theodor Wilrich
Keyword(s):  
Variance Component ◽  
Sampling Plans

MONITORING WESTERN FLOWER THRIPS (THYSANOPTERA: THRIPIDAE) IN “GRANNY SMITH” APPLE BLOSSOM CLUSTERS

1988 ◽  
Vol 120 (11) ◽  
pp. 1003-1016 ◽  
Author(s):  
L. Irene Terry ◽  
Gloria DeGrandi-Hoffman
Keyword(s):  
Variance Component ◽  
Frankliniella Occidentalis ◽  
Western Flower Thrips ◽  
Extraction Process ◽  
Regression Coefficients ◽  
Sampling Plans ◽  
Flower Thrips ◽  
Precision Level ◽  
Cluster Type ◽  
Over Time

AbstractThe efficiency and accuracy of sampling western flower thrips (Frankliniella occidentalis [Pergande]) from “Granny Smith” apple blossom clusters were analyzed during 1986–1987 to develop a sampling plan for research purposes. The accuracy of the “shake” method was compared with an “extraction” process of each of three blossom cluster types: pink, open, and petalless (petal fall). Thrip extractions from combined clusters revealed that a 9-s and 6-s “shake” removed 84 and 74%, of the thrips, respectively, but a 3-s “shake” removed 53%, and was more variable. Open blossom clusters always had higher thrips densities than either pink or petal fall clusters, regardless of the bloom state. The effects of cardinal position within trees were not consistent over time. Clusters from the top of the canopy had more thrips than lower canopy clusters, and apical clusters had more thrips than basal clusters during peak bloom. Variance component analyses indicated that thrips counts from clusters within tree were more variable than counts among trees, even when cluster types were analyzed separately. Two sets of indices (Iwao’s regression of mean crowding on mean density and Taylor’s regression of log variance on log mean density) for each cluster type indicated aggregated spatial patterns. Precision level sampling plans were developed using Iwao’s regression coefficients.

A New Acceptance Sampling Plans Based on Percentiles For Type II Generalized Half-logistic Distribution

2019 ◽  
Vol 10 (6) ◽  
pp. 1354-1363
Author(s):  
G Srinivasa Rao ◽  
Sd. Jilani ◽  
A Vasudeva Rao ◽  
S. Bhanu Prakash
Keyword(s):  
Logistic Distribution ◽  
Acceptance Sampling ◽  
Type Ii ◽  
Sampling Plans ◽  
Acceptance Sampling Plans

Quantifying idiosyncratic and shared contributions to stimulus evaluations

2018 ◽  
Author(s):  
Joel Eduardo Martinez ◽  
Friederike Funk ◽  
Alexander Todorov
Keyword(s):  
Repeated Measures ◽  
Variance Component ◽  
Psychological Problem ◽  
Psychological Research ◽  
Large Variability ◽  
Four Dimensions ◽  
Stimulus Evaluation ◽  
Crossed Random Effects ◽  
Novel Objects ◽  
Variance Component Estimates

A fundamental psychological problem is identifying the idiosyncratic and shared contributions to stimulus evaluation. However, there is no established method for estimating these contributions and the existing methods have led to divergent estimates. Moreover, in many studies participants rate the stimuli only once, although at least two measurements are required to estimate idiosyncratic contributions. Here, participants rated faces or novel objects on four dimensions (beautiful, approachable, likeable, dangerous) for a total of ten blocks to better estimate the preferences of individual raters. First, we show that both intra-rater and inter-rater agreement – measures related to idiosyncratic and shared contributions, respectively – increase with repeated measures. Second, to find best practices, we compared estimates from correlation indices and variance component approaches on stimulus-generality, evaluation-generality, data preprocessing steps, and sensitivity to measurement error (a largely ignored issue). The correlation indices changed monotonically and nonlinearly with more repeated measures. Variance component analyses showed large variability in estimates from only two repeated measures, but stabilized with more measures. While there was general agreement among approaches, the correlation approach was problematic for certain stimulus types and evaluation dimensions. Our results suggest that variance component estimates are more reliable as long as one collects more than two repeated measures, which is not the current norm in psychological research, and can be implemented using mixed models with crossed random effects. Recommendations for analysis and interpretations are provided.

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